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Epidemiologic, genetic, and neurobiological studies suggest considerable overlap between schizophrenia and mood disorders. Importantly, both disorders are associated with a broad range of cognitive deficits as well as altered glutamatergic and GABAergic neurometabolism. We conducted a systematic review of magnetic resonance spectroscopy (MRS) studies investigating the relationship between glutamatergic and GABAergic neurometabolites and cognition in schizophrenia spectrum disorders and mood disorders. A literature search in Pubmed of studies published before April 15, 2019 was conducted and 37 studies were deemed eligible for systematic review. We found that alterations in glutamatergic and GABAergic neurotransmission have been identified relatively consistently in both schizophrenia and mood disorders. However, because of the vast heterogeneity of published studies in terms of illness stage, medication exposure, MRS acquisition parameters and data post-processing strategies, we still do not understand the relationship between those neurotransmitters and cognitive dysfunction in mental illness, which is a critical initial step for rational drug development. Our findings emphasize the need for coordinated multi-center studies that characterize cognitive function and its biological substrates in large and well-defined clinical populations, using harmonized imaging sequences and analytical methods with the goal to elucidate the underlying pathophysiological mechanisms and to inform future clinical trials. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) are the two most common forms of neurodegenerative dementia. Although both of them have well-established diagnostic criteria, achieving early diagnosis remains challenging. Here, we aimed to make the differential diagnosis of AD and FTLD from clinical, neuropsychological, and neuroimaging features. Materials and methods: In this retrospective study, we selected 95 patients with PET-CT defined AD and 106 patients with PET-CT/biomarker-defined FTLD. We performed structured chart examination to collect clinical data and ascertain clinical features. A series of neuropsychological scales were used to assess the neuropsychological characteristics of patients. Automatic tissue segmentation of brain by Dr. Brain tool was used to collect multi-parameter volumetric measurements from different brain areas. All patients' structural neuroimage data were analyzed to obtain brain structure and white matter hyperintensities (WMH) quantitative data. Results: The prevalence of vascular disease associated factors was higher in AD patients than that in FTLD group. 56.84% of patients with AD carried at least one APOE +4 allele, which is much high than that in FTLD patients. The first symptoms of AD patients were mostly cognitive impairment rather than behavioral abnormalities. In contrast, behavioral abnormalities were the prominent early manifestations of FTLD, and few patients may be accompanied by memory impairment and motor symptoms. In direct comparison, patients with AD had slightly more posterior lesions and less frontal atrophy, whereas patients with FTLD had more frontotemporal atrophy and less posterior lesions. The WMH burden of AD was significantly higher, especially in cortical areas, while the WMH burden of FTLD was higher in periventricular areas. Conclusion: These results indicate that dynamic evaluation of cognitive function, behavioral and psychological symptoms, and multimodal neuroimaging are helpful for the early diagnosis and differentiation between AD and FTLD. [ABSTRACT FROM AUTHOR]

Recent advancements of multimodal neuroimaging such as functional MRI (fMRI) and diffusion MRI (dMRI) offers unprecedented opportunities to understand brain development. Most existing neurodevelopmental studies focus on using a single imaging modality to study microstructure or neural activations in localized brain regions. The developmental changes of brain network architecture in childhood and adolescence are not well understood. Our study made use of dMRI and resting-state fMRI imaging data sets from Philadelphia Neurodevelopmental Cohort (PNC) study to characterize developmental changes in both structural as well as functional brain connectomes. A multimodal multilevel model (MMM) is developed and implemented in PNC study to investigate brain maturation in both white matter structural connection and intrinsic functional connection. MMM addresses several major challenges in multimodal connectivity analysis. First, by using a first-level data generative model for observed measures and a second-level latent network modeling, MMM effectively infers underlying connection states from noisy imaging-based connectivity measurements. Second, MMM models the interplay between the structural and functional connections to capture the relationship between different brain connectomes. Third, MMM incorporates covariate effects in the network modeling to investigate network heterogeneity across subpopoulations. Finally, by using a module-wise parameterization based on brain network topology, MMM is scalable to whole-brain connectomics. MMM analysis of the PNC study generates new insights in neurodevelopment during adolescence including revealing the majority of the white fiber connectivity growth are related to the cognitive networks where the most significant increase is found between the default mode and the executive control network with a 15% increase in the probability of structural connections. We also uncover functional connectome development mainly derived from global functional integration rather than direct anatomical connections. To the best of our knowledge, these findings have not been reported in the literature using multimodal connectomics. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Multimodal neuroimaging plays an important role in neuroscience research. Integrated noninvasive neuroimaging modalities, such as magnetoencephalography (MEG), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), allow neural activity and related physiological processes in the brain to be precisely and comprehensively depicted, providing an effective and advanced platform to study brain function. Noncryogenic optically pumped magnetometer (OPM) MEG has high signal power due to its on-scalp sensor layout and enables more flexible configurations than traditional commercial superconducting MEG. Here, we integrate OPM-MEG with EEG and fNIRS to develop a multimodal neuroimaging system that can simultaneously measure brain electrophysiology and hemodynamics. We conducted a series of experiments to demonstrate the feasibility and robustness of our MEG-EEG-fNIRS acquisition system. The complementary neural and physiological signals simultaneously collected by our multimodal imaging system provide opportunities for a wide range of potential applications in neurovascular coupling, wearable neuroimaging, hyperscanning and brain-computer interfaces.

In this study, we examined the independent contributions of structural and functional connectivity markers to individual differences in episodic memory performance in 107 cognitively normal older adults from the BIOCARD study. Structural connectivity, defined by the diffusion tensor imaging (DTI) measure of radial diffusivity (RD), was obtained from two medial temporal lobe white matter tracts: the fornix and hippocampal cingulum, while functional connectivity markers were derived from network-based resting state functional magnetic resonance imaging (rsfMRI) of five large-scale brain networks: the control, default, limbic, dorsal attention, and salience/ventral attention networks. Hierarchical and stepwise linear regression methods were utilized to directly compare the relative contributions of the connectivity modalities to individual variability in a composite delayed episodic memory score, while also accounting for age, sex, cerebrospinal fluid (CSF) biomarkers of amyloid and tau pathology (i.e., Aβ42/Aβ40 and p-tau181), and gray matter volumes of the entorhinal cortex and hippocampus. Results revealed that fornix RD, hippocampal cingulum RD, and salience network functional connectivity were each significant independent predictors of memory performance, while CSF markers and gray matter volumes were not. Moreover, in the stepwise model, the addition of sex, fornix RD, hippocampal cingulum RD, and salience network functional connectivity each significantly improved the overall predictive value of the model. These findings demonstrate that both DTI and rsfMRI connectivity measures uniquely contributed to the model and that the combination of structural and functional connectivity markers best accounted for individual variability in episodic memory function in cognitively normal older adults. [ABSTRACT FROM AUTHOR]

Premenstrual syndrome (PMS) is a group of psychological, physical, and behavioral symptoms that recur with the menstrual cycle, usually occurring a few days before menstruation and ceasing with the onset of menstruation. Premenstrual dysphoric disorder (PMDD) is a severe form of PMS that has been included in a subcategory of depression in the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) according to the latest diagnostic criteria. Patients usually present with mild to moderate emotional and physical symptoms that affect their routine work, social activities, and family lives. The pathogenesis of PMDD remains unclear, and some researchers believe that it is related to fluctuations in ovarian hormone levels. However, the details of the interrelationships and regulating effects between ovarian hormones, symptoms, and the brain need to be more comprehensively determined. Recent studies have revealed some novel findings on PMS and PMDD based on brainmorphology, function, andmetabolism. Additionally, multiple studies have suggested that PMS and PMDD are closely related to brain structural and functional variations in certain core temporal lobe regions, such as the amygdala and hippocampus. We summarized neuroimaging studies of PMS and PMDD related to the temporal lobe by retrospectively reviewing relevant literature over the past decade. This review contributes to further clarifying the significant role of the temporal lobe in PMS and PMDD and understanding the neurochemical links between hormones, symptoms, and the brain. [ABSTRACT FROM AUTHOR]

In this study, we examined the independent contributions of structural and functional connectivity markers to individual differences in episodic memory performance in 107 cognitively normal older adults from the BIOCARD study. Structural connectivity, defined by the diffusion tensor imaging (DTI) measure of radial diffusivity (RD), was obtained from two medial temporal lobe white matter tracts: the fornix and hippocampal cingulum, while functional connectivity markers were derived from network-based resting state functional magnetic resonance imaging (rsfMRI) of five large-scale brain networks: the control, default, limbic, dorsal attention, and salience/ventral attention networks. Hierarchical and stepwise linear regression methods were utilized to directly compare the relative contributions of the connectivity modalities to individual variability in a composite delayed episodic memory score, while also accounting for age, sex, cerebrospinal fluid (CSF) biomarkers of amyloid and tau pathology (i.e., Aβ42/Aβ40 and p-tau181), and gray matter volumes of the entorhinal cortex and hippocampus. Results revealed that fornix RD, hippocampal cingulum RD, and salience network functional connectivity were each significant independent predictors of memory performance, while CSF markers and gray matter volumes were not. Moreover, in the stepwise model, the addition of sex, fornix RD, hippocampal cingulum RD, and salience network functional connectivity each significantly improved the overall predictive value of the model. These findings demonstrate that both DTI and rsfMRI connectivity measures uniquely contributed to the model and that the combination of structural and functional connectivity markers best accounted for individual variability in episodic memory function in cognitively normal older adults.

Recent functional magnetic resonance imaging (fMRI) studies revealed lower neural activation during processing of an n-back task following working memory training, indicating a training-related increase in neural efficiency. In the present study, we asked if the training induced regional neural activation is accompanied by changes in glucose consumption. An active control and an experimental group of healthy middle-aged volunteers conducted 32 sessions of visual and verbal n-back trainings over 8 weeks. We analyzed data of 52 subjects (25 experimental and 27 control group) for practice effects underlying verbal working memory task and 50 subjects (24 experimental and 26 control group) for practice effects underlying visual WM task. The samples of these two tasks were nearly identical (data of 47 subjects were available for both verbal and visual tasks). Both groups completed neuroimaging sessions at a hybrid PET/MR system before and after training. Each session included criterion task fMRI and resting state positron emission tomography with FDG (FDG-PET). As reported previously, lower neural activation following n-back training was found in regions of the fronto-parieto-cerebellar circuitry during a verbal n-back task. Notably, these changes co-occurred spatially with a higher relative FDG-uptake. Decreased neural activation within regions of the fronto-parietal network during visual n-back task did not show co-occurring changes in relative FDG-uptake. There was no direct association between neuroimaging and behavioral measures, which could be due to the inter-subjects’ variability in reaching capacity limits. Our findings provide new details for working memory training induced neural efficiency on a molecular level by integrating FDG-PET and fMRI measures.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition, which is accompanied by differences in gray matter neuroanatomy and white matter connectivity. However, it is unknown whether these differences are linked or reflect independent aetiologies. Using a multimodal neuroimaging approach, we therefore examined 51 male adults with ASD and 48 neurotypical controls to investigate the relationship between gray matter local gyrification (lGI) and white matter diffusivity in associated fiber tracts. First, ASD individuals had a significant increase in gyrification around the left pre- and post-central gyrus. Second, white matter fiber tracts originating and/or terminating in the cluster of increased lGI had a significant increase in axial diffusivity. This increase in diffusivity was predominantly observed in tracts in close proximity to the cortical sheet. Last, we demonstrate that the increase in lGI was significantly correlated with increased diffusivity of short tracts. This relationship was not significantly modulated by a main effect of group (i.e., ASD), which was more closely associated with gray matter gyrification than white matter diffusivity. Our findings suggest that differences in gray matter neuroanatomy and white matter connectivity are closely linked, and may reflect common rather than distinct aetiological pathways.

Alzheimer's disease involves many neurobiological alterations from molecular to macroscopic spatial scales, but we currently lack integrative, mechanistic brain models characterizing how factors across different biological scales interact to cause clinical deterioration in a way that is subject-specific or personalized. As important signalling molecules and mediators of many neurobiological interactions, neurotransmitter receptors are promising candidates for identifying molecular mechanisms and drug targets in Alzheimer's disease. We present a neurotransmitter receptor-enriched multifactorial brain model, which integrates spatial distribution patterns of 15 neurotransmitter receptors from post-mortem autoradiography with multiple in vivo neuroimaging modalities (tau, amyloid-β and glucose PET, and structural, functional and arterial spin labelling MRI) in a personalized, generative, whole-brain formulation. In a heterogeneous aged population (n = 423, ADNI data), models with personalized receptor-neuroimaging interactions showed a significant improvement over neuroimaging-only models, explaining about 70% (±20%) of the variance in longitudinal changes to the six neuroimaging modalities. In Alzheimer's disease patients (n = 25, ADNI data), receptor-imaging interactions explained up to 39.7% (P < 0.003, family-wise error-rate-corrected) of inter-individual variability in cognitive deterioration, via an axis primarily affecting executive function. Notably, based on their contribution to the clinical severity in Alzheimer's disease, we found significant functional alterations to glutamatergic interactions affecting tau accumulation and neural activity dysfunction and GABAergic interactions concurrently affecting neural activity dysfunction, amyloid and tau distributions, as well as significant cholinergic receptor effects on tau accumulation. Overall, GABAergic alterations had the largest effect on cognitive impairment (particularly executive function) in our Alzheimer's disease cohort (n = 25). Furthermore, we demonstrate the clinical applicability of this approach by characterizing subjects based on individualized 'fingerprints' of receptor alterations. This study introduces the first robust, data-driven framework for integrating several neurotransmitter receptors, multimodal neuroimaging and clinical data in a flexible and interpretable brain model. It enables further understanding of the mechanistic neuropathological basis of neurodegenerative progression and heterogeneity, and constitutes a promising step towards implementing personalized, neurotransmitter-based treatments. [ABSTRACT FROM AUTHOR]

Image‐on‐image regression analysis, using images to predict images, is a challenging task, due to (1) the high dimensionality and (2) the complex spatial dependence structures in image predictors and image outcomes. In this work, we propose a novel image‐on‐image regression model, by extending a spatial Bayesian latent factor model to image data, where low‐dimensional latent factors are adopted to make connections between high‐dimensional image outcomes and image predictors. We assign Gaussian process priors to the spatially varying regression coefficients in the model, which can well capture the complex spatial dependence among image outcomes as well as that among the image predictors. We perform simulation studies to evaluate the out‐of‐sample prediction performance of our method compared with linear regression and voxel‐wise regression methods for different scenarios. The proposed method achieves better prediction accuracy by effectively accounting for the spatial dependence and efficiently reduces image dimensions with latent factors. We apply the proposed method to analysis of multimodal image data in the Human Connectome Project where we predict task‐related contrast maps using subcortical volumetric seed maps. [ABSTRACT FROM AUTHOR]

Backgrounds: .Brain morphological biomarkers could contribute to understanding the treatment response in patients with obsessive-compulsive disorder (OCD). Multimodal neuroimaging addresses this issue by providing more comprehensive information regarding neural processes and structures. Objectives. The present study aims to investigate whether patients responsive to deep Transcranial Magnetic Stimulation (TMS) differ from non-responsive individuals in terms of electrophysiology and brain morphology. Secondly, to test whether multimodal neuroimaging is superior to unimodal neuroimaging in predicting response to deep TMS. Methods. Thirty-two OCD patients who underwent thirty sessions of deep TMS treatment were included in the study. Based on a minimum 50% reduction in Yale-Brown Obsessive Compulsive Scale (Y-BOCS) scores after treatment, patients were grouped as responders (n = 25) and non-responders (n = 7). The baseline resting state qEEG and magnetic resonance imaging (MRI) records of patients were recorded. Independent sample t-test is used to compare the groups. Then, three logistic regression model were calculated for only QEEG markers, only MRI markers, and both QEEG/MRI markers. The predictive values of the three models were compared. Results. OCD patients who responded to deep TMS treatment had increased Alpha-2 power in the left temporal area and increased volume in the left temporal pole, entorhinal area, and parahippocampal gyrus compared to non-responders. The logistic regression model showed better prediction performance when both QEEG and MRI markers were included. Conclusions. This study addresses the gap in the literature regarding new functional and structural neuroimaging markers and highlights the superiority of multimodal neuroimaging to unimodal neuroimaging techniques in predicting treatment response., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Heavy alcohol use and its associated conditions, such as alcohol use disorder, impact millions of individuals worldwide. While our understanding of the neurobiological correlates of alcohol use has evolved substantially, we still lack models that incorporate whole-brain neuroanatomical, functional, and pharmacological information under one framework., Methods: Here, we utilized diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in 130 individuals with a high amount of current alcohol use. We compared these alcohol-using individuals to 308 individuals with minimal use of any substances., Results: We found that individuals with heavy alcohol use had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Furthermore, using separately acquired positron emission tomography data, we deployed an in silico evaluation demonstrating that decreased D 2 receptor levels, as found previously in individuals with alcohol use disorder, may relate to our observed findings., Conclusions: This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of heavy alcohol use., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Rett syndrome (RTT) is a rare neurodevelopmental disorder characterized by severe cognitive, social, and physical impairments resulting from de novo mutations in the X-chromosomal methyl-CpG binding protein gene 2 (MECP2). While there is still no cure for RTT, exploring up-to date neurofunctional diagnostic markers, discovering new potential therapeutic targets, and searching for novel drug efficacy evaluation indicators are fundamental. Multiple neuroimaging studies on brain structure and function have been carried out in RTT-linked gene mutation carriers to unravel disease-specific imaging features and explore genotype-phenotype associations. Here, we reviewed the neuroimaging literature on this disorder. MRI morphologic studies have shown global atrophy of gray matter (GM) and white matter (WM) and regional variations in brain maturation. Diffusion tensor imaging (DTI) studies have demonstrated reduced fractional anisotropy (FA) in left peripheral WM areas, left major WM tracts, and cingulum bilaterally, and WM microstructural/network topology changes have been further found to be correlated with behavioral abnormalities in RTT. Cerebral blood perfusion imaging studies using single-photon emission CT (SPECT) or PET have evidenced a decreased global cerebral blood flow (CBF), particularly in prefrontal and temporoparietal areas, while magnetic resonance spectroscopy (MRS) and PET studies have contributed to unraveling metabolic alterations in patients with RTT. The results obtained from the available reports confirm that multimodal neuroimaging can provide new insights into a complex interplay between genes, neurotransmitter pathway abnormalities, disease-related behaviors, and clinical severity. However, common limitations related to the available studies include small sample sizes and hypothesis-based and region-specific approaches. We, therefore, conclude that this field is still in its early development phase and that multimodal/multisequence studies with improved post-processing technologies as well as combined PET–MRI approaches are urgently needed to further explore RTT brain alterations.

The concept of cognitive reserve (CR) has been proposed as a protective factor that modifies the effect of brain pathology on cognitive performance. It has been characterized through CR proxies; however, they have intrinsic limitations. In this study, we utilized two different datasets containing tau, amyloid PET, and T1 magnetic resonance imaging. First, 91 Alzheimer’s disease (AD) continuum subjects were included from Alzheimer’s Disease Neuroimaging Initiative 3. CR was conceptualized as the residual between actual cognition and estimated cognition based on amyloid, tau, and neurodegeneration. The proposed marker was tested by the correlation with CR proxy and modulation of brain pathology effects on cognitive function. Second, longitudinal data of baseline 53 AD spectrum and 34 cognitively unimpaired (CU) participants in the MEMORI dataset were analyzed. CR marker was evaluated for the association with disease conversion rate and clinical progression. Applying our multimodal CR model, this study demonstrates the differential effect of CR on clinical progression according to the disease status and the modulating effect on the relationship between brain pathology and cognition. The proposed marker was associated with years of education and modulated the effect of pathological burden on cognitive performance in the AD spectrum. Longitudinally, higher CR marker was associated with lower disease conversion rate among prodromal AD and CU individuals. Higher CR marker was related to exacerbated cognitive decline in the AD spectrum; however, it was associated with a mitigated decline in CU individuals. These results provide evidence that CR may affect the clinical progression differentially depending on the disease status.

Rett syndrome (RTT) is a rare neurodevelopmental disorder characterized by severe cognitive, social, and physical impairments resulting from de novo mutations in the X-chromosomal methyl-CpG binding protein gene 2 (MECP2). While there is still no cure for RTT, exploring up-to date neurofunctional diagnostic markers, discovering new potential therapeutic targets, and searching for novel drug efficacy evaluation indicators are fundamental. Multiple neuroimaging studies on brain structure and function have been carried out in RTT-linked gene mutation carriers to unravel disease-specific imaging features and explore genotype-phenotype associations. Here, we reviewed the neuroimaging literature on this disorder. MRI morphologic studies have shown global atrophy of gray matter (GM) and white matter (WM) and regional variations in brain maturation. Diffusion tensor imaging (DTI) studies have demonstrated reduced fractional anisotropy (FA) in left peripheral WM areas, left major WM tracts, and cingulum bilaterally, and WM microstructural/network topology changes have been further found to be correlated with behavioral abnormalities in RTT. Cerebral blood perfusion imaging studies using single-photon emission CT (SPECT) or PET have evidenced a decreased global cerebral blood flow (CBF), particularly in prefrontal and temporoparietal areas, while magnetic resonance spectroscopy (MRS) and PET studies have contributed to unraveling metabolic alterations in patients with RTT. The results obtained from the available reports confirm that multimodal neuroimaging can provide new insights into a complex interplay between genes, neurotransmitter pathway abnormalities, disease-related behaviors, and clinical severity. However, common limitations related to the available studies include small sample sizes and hypothesis-based and region-specific approaches. We, therefore, conclude that this field is still in its early development phase and that multimodal/multisequence studies with improved post-processing technologies as well as combined PET–MRI approaches are urgently needed to further explore RTT brain alterations. [ABSTRACT FROM AUTHOR]

The concept of cognitive reserve (CR) has been proposed as a protective factor that modifies the effect of brain pathology on cognitive performance. It has been characterized through CR proxies; however, they have intrinsic limitations. In this study, we utilized two different datasets containing tau, amyloid PET, and T1 magnetic resonance imaging. First, 91 Alzheimer's disease (AD) continuum subjects were included from Alzheimer's Disease Neuroimaging Initiative 3. CR was conceptualized as the residual between actual cognition and estimated cognition based on amyloid, tau, and neurodegeneration. The proposed marker was tested by the correlation with CR proxy and modulation of brain pathology effects on cognitive function. Second, longitudinal data of baseline 53 AD spectrum and 34 cognitively unimpaired (CU) participants in the MEMORI dataset were analyzed. CR marker was evaluated for the association with disease conversion rate and clinical progression. Applying our multimodal CR model, this study demonstrates the differential effect of CR on clinical progression according to the disease status and the modulating effect on the relationship between brain pathology and cognition. The proposed marker was associated with years of education and modulated the effect of pathological burden on cognitive performance in the AD spectrum. Longitudinally, higher CR marker was associated with lower disease conversion rate among prodromal AD and CU individuals. Higher CR marker was related to exacerbated cognitive decline in the AD spectrum; however, it was associated with a mitigated decline in CU individuals. These results provide evidence that CR may affect the clinical progression differentially depending on the disease status. [ABSTRACT FROM AUTHOR]

Introduction: Physical inactivity and female sex are independently associated with increased Alzheimer's disease (AD) lifetime risk. This study investigates the possible interactions between sex and physical activity on neuroimaging biomarkers. Methods: In 134 cognitively unimpaired older adults (≥65 years, 82 women) from the Age‐Well randomized controlled trial (baseline data), we investigated the association between physical activity and multimodal neuroimaging (gray matter volume, glucose metabolism, perfusion, and amyloid burden), and how sex modulates these associations. Results: The anterior cingulate cortex volume was independently associated with sex and physical activity. Sex and physical activity interacted on perfusion and amyloid deposition in medial parietal regions, such that physical activity was related to perfusion only in women, and to amyloid burden only in men. Discussion: Physical activity has both sex‐dependent and sex‐independent associations with brain integrity. Our findings highlight partly distinct reserve mechanisms in men and women, which might in turn influence their risk of AD. Highlights: Sex and physical activity have been linked to Alzheimer's disease (AD) progression.The association of sex and physical activity with brain health is partly independent.Different reserve mechanisms exist in men and women. [ABSTRACT FROM AUTHOR]

The link between spatial (where) and temporal (when) aspects of the neural correlates of most psychological phenomena is not clear. Elucidation of this relation, which is crucial to fully understand human brain function, requires integration across multiple brain imaging modalities and cognitive tasks that reliably modulate the engagement of the brain systems of interest. By overcoming the methodological challenges posed by simultaneous recordings, the present report provides proof‐of‐concept evidence for a novel approach using three complementary imaging modalities: functional magnetic resonance imaging (fMRI), event‐related potentials (ERPs), and event‐related optical signals (EROS). Using the emotional oddball task, a paradigm that taps into both cognitive and affective aspects of processing, we show the feasibility of capturing converging and complementary measures of brain function that are not currently attainable using traditional unimodal or other multimodal approaches. This opens up unprecedented possibilities to clarify spatiotemporal integration of brain function. [ABSTRACT FROM AUTHOR]

Circadian rhythms are considered a masterstroke of natural selection, which gradually increase the adaptability of species to the Earth's rotation. Importantly, the nervous system plays a key role in allowing organisms to maintain circadian rhythmicity. Circadian rhythms affect multiple aspects of cognitive functions (mainly via arousal), particularly those needed for effort-intensive cognitive tasks, which require considerable top-down executive control. These include inhibitory control, working memory, task switching, and psychomotor vigilance. This mini review highlights the recent advances in cognitive functioning in the optical and multimodal neuroimaging fields; it discusses the processing of brain cognitive functions during the circadian rhythm phase and the effects of the circadian rhythm on the cognitive component of the brain and the brain circuit supporting cognition. [ABSTRACT FROM AUTHOR]

Data on human brain function obtained with direct electrical stimulation (DES) in neurosurgical patients have been recently integrated and combined with modern neuroimaging techniques, allowing a connectome-based approach fed by intraoperative DES data. Within this framework is crucial to develop reliable methods for spatial localization of DES-derived information to be integrated within the neuroimaging workflow. To this aim, we applied the Kernel Density Estimation for modelling the distribution of DES sites from different patients into the MNI space. The algorithm has been embedded in a MATLAB-based User Interface, Peaglet. It allows an accurate probabilistic weighted and unweighted estimation of DES sites location both at cortical level, by using shortest path calculation along the brain 3D geometric topology, and subcortical level, by using a volume-based approach. We applied Peaglet to investigate spatial estimation of cortical and subcortical stimulation sites provided by recent brain tumour studies. The resulting NIfTI maps have been anatomically investigated with neuroimaging open-source tools. Peaglet processes differently cortical and subcortical data following their distinguishing geometrical features, increasing anatomical specificity of DES-related results and their reliability within neuroimaging environments. Peaglet provides a robust probabilistic estimation of the cortical and subcortical distribution of DES sites going beyond a region of interest approach, respecting cortical and subcortical intrinsic geometrical features. Results can be easily integrated within the neuroimaging workflow to drive connectomic analysis. • Peaglet is a matlab software allowing a spatial estimation of stimulation sites in the MNI space. • Dedicated cortical and subcortical analysis can be applied for spatial estimation. • Peaglet results can be visualised and inspected with common open-source software. • Peaglet results can be used to build ROIs to drive neuroimaging analysis. [ABSTRACT FROM AUTHOR]

OBJECTIVE In this study we investigated the stimulant methylphenidate (MPH) effects in Attention deficit hyperactivity disorder (ADHD) from neuroimaging and neurophysiological perspective by simultaneous recording functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) during attention task. METHODS Using fNIRS we obtained frontal cortex hemodynamic responses and using event related potentials (ERP) we obtained amplitude values of P3 component of 18 children with ADHD and gender matched 18 healthy controls performing an oddball task. Same recordings were repeated 3 months after extended-release MPH (OROS-MPH) administration for ADHD group. Prefrontal cortex oxygenation and P3 amplitude were compared between control and pre-MPH ADHD groups and between Pre-MPH and post-MPH ADHD groups. RESULTS fNIRS indicated that the healthy controls exhibited higher right prefrontal activation than pre-MPH children with ADHD. Reduced P3 amplitude values were found in children with ADHD compared the control group. Reduced right prefrontal activation and P3 amplitude was normalized in ADHD group after MPH therapy. CONCLUSION Recently multimodal neuroimaging which combine signals from different brain modalities have started to be considered as a potential to improve the accuracy of diagnosis. The current study provides MPH effect assessment in children with ADHD using multimodal EEG/fNIRS system for the first time. This study suggests combination of neuroimaging and electrophysiological parameters is a promising approach to investigate MPH effect assessment in children with ADHD.

Abstract Background Acupuncture is effective in functional constipation (FC) treatment, but the central mechanism has not been well investigated. This trial will combine functional magnetic resonance imaging (fMRI) and positron emission tomography-computed tomography (PET-CT) to investigate the potential central mechanism of acupuncture treatment for FC. Methods This is a multimodal neuroimaging randomized controlled trial. In total, 140 FC patients will be randomly allocated into four groups: the verum acupuncture group; the sham acupuncture group; the PEG 4000 group; and the waiting-list group. This trial will include a two-week baseline period and a two-week treatment period. Patients will receive 10 sessions of acupuncture, sham acupuncture, PEG 4000, or no intervention during the treatment period. The stool diary, Cleveland Constipation Score (CCS), Patient Assessment of Constipation Symptom (PAC-SYM), and Patient Assessment of Constipation Quality of Life Questionnaire (PAC-QoL) will be used to assess the clinical efficacy of different interventions. The MRI and PET-CT scans will be performed to detect cerebral functional changes in 15 patients in each group at baseline and at the end of treatment/waiting. Multimodal imaging data will be associated with clinical data to investigate possible correlation between brain activity changes elicited by different interventions and symptoms improvement. Discussion We hypothesize that acupuncture can treat FC through normalizing the pathological alteration of the cerebral activity. The results of this trial will allow us to re-testify the therapeutic effects of acupuncture treating for FC and to investigate the potential central mechanism of acupuncture treatment for FC from direct (cerebral glucose metabolism) and indirect (contrast of oxyhemoglobin and deoxyhemoglobin) approaches. Trial registration Chinese Clinical Trial Registry, ChiCTR1800016658. Registered on 14 June 2018.

Electroencephalographic (EEG) neurofeedback (NFB) is a popular neuromodulation method to help one selectively enhance or inhibit his/her brain activities by means of real-time visual or auditory feedback of EEG signals. Sensory motor rhythm (SMR) NFB protocol has been applied to improve cognitive performance, but a large proportion of participants failed to self-regulate their brain activities and could not benefit from NFB training. Therefore, it is important to identify the neural predictors of SMR up-regulation NFB training performance for a better understanding the mechanisms of individual difference in SMR NFB. Twenty-seven healthy participants (12 males, age: 23.1 ± 2.36) were enrolled to complete three sessions of SMR up-regulation NFB training and collection of multimodal neuroimaging data [resting-state EEG, structural magnetic resonance imaging (MRI), and resting-state functional MRI (fMRI)]. Correlation analyses were performed between within-session NFB learning index and anatomical and functional brain features extracted from multimodal neuroimaging data, in order to identify the neuroanatomical and neurophysiological predictors for NFB learning performance. Lastly, machine learning models were trained to predict NFB learning performance using features from each modality as well as multimodal features. According to our results, most participants were able to successfully increase the SMR power and the NFB learning performance was significantly correlated with a set of neuroimaging features, including resting-state EEG powers, gray/white matter volumes from MRI, regional and functional connectivity (FC) of resting-state fMRI. Importantly, results of prediction analysis indicate that NFB learning index can be better predicted using multimodal features compared with features of single modality. In conclusion, this study highlights the importance of multimodal neuroimaging technique as a tool to explain the individual difference in within-session NFB learning performance, and could provide a theoretical framework for early identification of individuals who cannot benefit from NFB training.

Delineating the relationship between human neurodevelopment and the maturation of the hypothalamic-pituitary-gonadal (HPG) axis during puberty is critical for investigating the increase in vulnerability to neuropsychiatric disorders that is well documented during this period. Preclinical research demonstrates a clear association between gonadal production of sex steroids and neurodevelopment; however, identifying similar associations in humans has been complicated by confounding variables (such as age) and the coactivation of two additional endocrine systems (the adrenal androgenic system and the somatotropic growth axis) and requires further elucidation. In this paper, we present the design of, and preliminary observations from, the ongoing NIMH Intramural Longitudinal Study of the Endocrine and Neurobiological Events Accompanying Puberty. The aim of this study is to directly examine how the increase in sex steroid hormone production following activation of the HPG-axis (i.e., gonadarche) impacts neurodevelopment, and, additionally, to determine how gonadal development and maturation is associated with longitudinal changes in brain structure and function in boys and girls. To disentangle the effects of sex steroids from those of age and other endocrine events on brain development, our study design includes 1) selection criteria that establish a well-characterized baseline cohort of healthy 8-year-old children prior to the onset of puberty (e.g., prior to puberty-related sex steroid hormone production); 2) temporally dense longitudinal, repeated-measures sampling of typically developing children at 8-10 month intervals over a 10-year period between the ages of eight and 18; 3) contemporaneous collection of endocrine and other measures of gonadal, adrenal, and growth axis function at each timepoint; and 4) collection of multimodal neuroimaging measures at these same timepoints, including brain structure (gray and white matter volume, cortical thickness and area, white matter integrity, myelination) and function (reward processing, emotional processing, inhibition/impulsivity, working memory, resting-state network connectivity, regional cerebral blood flow). This report of our ongoing longitudinal study 1) provides a comprehensive review of the endocrine events of puberty; 2) details our overall study design; 3) presents our selection criteria for study entry (e.g., well-characterized prepubertal baseline) along with the endocrinological considerations and guiding principles that underlie these criteria; 4) describes our longitudinal outcome measures and how they specifically relate to investigating the effects of gonadal development on brain development; and 5) documents patterns of fMRI activation and resting-state networks from an early, representative subsample of our cohort of prepubertal 8-year-old children.

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision.

Electroencephalographic (EEG) neurofeedback (NFB) is a popular neuromodulation method to help one selectively enhance or inhibit his/her brain activities by means of real-time visual or auditory feedback of EEG signals. Sensory motor rhythm (SMR) NFB protocol has been applied to improve cognitive performance, but a large proportion of participants failed to self-regulate their brain activities and could not benefit from NFB training. Therefore, it is important to identify the neural predictors of SMR up-regulation NFB training performance for a better understanding the mechanisms of individual difference in SMR NFB. Twenty-seven healthy participants (12 males, age: 23.1 ± 2.36) were enrolled to complete three sessions of SMR up-regulation NFB training and collection of multimodal neuroimaging data [resting-state EEG, structural magnetic resonance imaging (MRI), and resting-state functional MRI (fMRI)]. Correlation analyses were performed between within-session NFB learning index and anatomical and functional brain features extracted from multimodal neuroimaging data, in order to identify the neuroanatomical and neurophysiological predictors for NFB learning performance. Lastly, machine learning models were trained to predict NFB learning performance using features from each modality as well as multimodal features. According to our results, most participants were able to successfully increase the SMR power and the NFB learning performance was significantly correlated with a set of neuroimaging features, including resting-state EEG powers, gray/white matter volumes from MRI, regional and functional connectivity (FC) of resting-state fMRI. Importantly, results of prediction analysis indicate that NFB learning index can be better predicted using multimodal features compared with features of single modality. In conclusion, this study highlights the importance of multimodal neuroimaging technique as a tool to explain the individual difference in within-session NFB learning performance, and could provide a theoretical framework for early identification of individuals who cannot benefit from NFB training. [ABSTRACT FROM AUTHOR]

[Display omitted] o Machine learning has been widely applied to MRI to detect and diagnose gliomas. o Non-invasive detection of genetics has the potential to supersede biopsy procedures. o Machine learning has demonstrated strong performance and clinical utility. o Performance metrics from 153 articles were synthesized to facilitate benchmarking. o Deep language models can facilitate systematic review article screening. Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 ± 0.09; sensitivity = 0.87 ± 0.10; specificity = 0.0.86 ± 0.10; precision = 0.88 ± 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision. [ABSTRACT FROM AUTHOR]

Medulloblastoma is a malign posterior fossa brain tumor, mostly occurring in childhood. The CNS-directed chemoradiotherapy treatment can be very harmful to the developing brain and functional outcomes of these patients. However, what the underlying neurotoxic mechanisms are remain inconclusive. Hence, this review summarizes the existing literature on the association between advanced neuroimaging and neurocognitive changes in patients that were treated for pediatric medulloblastoma. The PubMed/Medline database was extensively screened for studies investigating the link between cognitive outcomes and multimodal magnetic resonance (MR) imaging in childhood medulloblastoma survivors. A behavioral meta-analysis was performed on the available IQ scores. A total of 649 studies were screened, of which 22 studies were included. Based on this literature review, we conclude medulloblastoma patients to be at risk for white matter volume loss, more frequent white matter lesions, and changes in white matter microstructure. Such microstructural alterations were associated with lower IQ, which reached the clinical cut-off in survivors across studies. Using functional MR scans, changes in activity were observed in cerebellar areas, associated with working memory and processing speed. Finally, cerebral microbleeds were encountered more often, but these were not associated with cognitive outcomes. Regarding intervention studies, computerized cognitive training was associated with changes in prefrontal and cerebellar activation and physical training might result in microstructural and cortical alterations. Hence, to better define the neural targets for interventions in pediatric medulloblastoma patients, this review suggests working towards neuroimaging-based predictions of cognitive outcomes. To reach this goal, large multimodal prospective imaging studies are highly recommended. [ABSTRACT FROM AUTHOR]

Background: As the population ages, maintaining mental health and well-being of older adults is a public health priority. Beyond objective measures of health, self-perceived quality of life (QoL) is a good indicator of successful aging. In older adults, it has been shown that QoL is related to structural brain changes. However, QoL is a multi-faceted concept and little is known about the specific relationship of each QoL domain to brain structure, nor about the links with other aspects of brain integrity, including white matter microstructure, brain perfusion and amyloid deposition, which are particularly relevant in aging. Therefore, we aimed to better characterize the brain biomarkers associated with each QoL domain using a comprehensive multimodal neuroimaging approach in older adults. Methods: One hundred and thirty-five cognitively unimpaired older adults (mean age ± SD: 69.4 ± 3.8 y) underwent structural and diffusion magnetic resonance imaging, together with early and late florbetapir positron emission tomography scans. QoL was assessed using the brief version of the World Health Organization's QoL instrument, which allows measuring four distinct domains of QoL: self-perceived physical health, psychological health, social relationships and environment. Multiple regression analyses were carried out to identify the independent global neuroimaging predictor(s) of each QoL domain, and voxel-wise analyses were then conducted with the significant predictor(s) to highlight the brain regions involved. Age, sex, education and the other QoL domains were entered as covariates in these analyses. Finally, forward stepwise multiple regressions were conducted to determine the specific items of the relevant QoL domain(s) that contributed the most to these brain associations. Results: Only physical health QoL was associated with global neuroimaging values, specifically gray matter volume and white matter mean kurtosis, with higher physical health QoL being associated with greater brain integrity. These relationships were still significant after correction for objective physical health and physical activity measures. No association was found with global brain perfusion or global amyloid deposition. Voxel-wise analyses revealed that the relationships with physical health QoL concerned the anterior insula and ventrolateral prefrontal cortex, and the corpus callosum, corona radiata, inferior frontal white matter and cingulum. Self-perceived daily living activities and self-perceived pain and discomfort were the items that contributed the most to these associations with gray matter volume and white matter mean kurtosis, respectively. Conclusions: Better self-perceived physical health, encompassing daily living activities and pain and discomfort, was the only QoL domain related to brain structural integrity including higher global gray matter volume and global white matter microstructural integrity in cognitively unimpaired older adults. The relationships involved brain structures belonging to the salience network, the pain pathway and the empathy network. While previous studies showed a link between objective measures of physical health, our findings specifically highlight the relevance of monitoring and promoting self-perceived physical health in the older population. Longitudinal studies are needed to assess the direction and causality of the relationships between QoL and brain integrity.

Multimodal neuroimaging studies combining proton magnetic resonance spectroscopy (1H-MRS) to quantify GABA and/or glutamate concentrations and functional magnetic resonance imaging (fMRI) to measure brain activity non-invasively have advanced understanding of how neurochemistry and neurophysiology may be related at a macroscopic level. The present study aimed to perform a systematic review and meta-analysis of available studies examining the relationship between 1H-MRS glutamate and/or GABA levels and task-related fMRI signal in the healthy brain. Ovid (Medline, Embase, and PsycINFO) and Pubmed databases were systematically searched to identify articles published until December 2019. The primary outcome of interest was the association between resting levels of glutamate or GABA and task-related fMRI. Fifty-five papers were identified for inclusion in the systematic review. A further 22 studies were entered into four separate meta-analyses. These meta-analyses found evidence of significant negative associations between local GABA levels and (a) fMRI activation to visual tasks in the occipital lobe, and (b) activation to emotion processing in the medial prefrontal cortex (mPFC)/anterior cingulate cortex (ACC). However, there was no significant association between mPFC/ACC glutamate levels and fMRI activation to cognitive control tasks or to emotional processing, with the relationship to emotion processing related neural activity narrowly missing significance. Moreover, our systematic review also found converging evidence of negative associations between GABA levels and local brain activity, and positive associations between glutamate levels and distal brain activity, outside of the 1H-MRS sampling region. Albeit less consistently, additional relationships between GABA levels and distal brain activity and between glutamate levels and local brain activity were found. It remains unclear if the absence of effects for other brain regions and other cognitive-emotional domains reflects study heterogeneity or potential confounding effects of age, sex, or other unknown factors. Advances in 1H-MRS methodology as well as in the integration of 1H-MRS readouts with other imaging modalities for indexing neural activity hold great potential to reveal key aspects of the pathophysiology of mental health disorders involving aberrant interactions between neurochemistry and neurophysiology such as schizophrenia.

A new paradigm for human brain imaging, mobile brain/body imaging (MoBI), involves synchronous collection of human brain activity (via electroencephalography, EEG) and behavior (via body motion capture, eye tracking, etc.), plus environmental events (scene and event recording) to study joint brain/body dynamics supporting natural human cognition supporting performance of naturally motivated human actions and interactions in 3-D environments (Makeig et al., 2009). Processing complex, concurrent, multi-modal, multi-rate data streams requires a signal-processing environment quite different from one designed to process single-modality time series data. Here we describe MoBILAB (more details available at sccn.ucsd.edu/wiki/MoBILAB), an open source, cross platform toolbox running on MATLAB (The Mathworks, Inc.) that supports analysis and visualization of any mixture of synchronously recorded brain, behavioral, and environmental time series plus time-marked event stream data. MoBILAB can serve as a pre-processing environment for adding behavioral and other event markers to EEG data for further processing, and/or as a development platform for expanded analysis of simultaneously recorded data streams.

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS–EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS–EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS–EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS–EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS–EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS–EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS–EEG data analyses in future research.

Depression is a highly recurrent disorder. When in remission, it affords an important opportunity to understand the state-independent neurobiological alterations, as well as the socio-demographic characteristics, that likely contribute to the recurrence of major depressive disorder (MDD). The present study examined 110 euthymic women in their early postpartum period. A comparison was made between participants with (n = 20) and without (n = 90) a history of MDD by means of a multimodal approach including an fMRI experiment, assessment of hair cortisol concentration (HCC) and a clinical anamnestic interview. Women with a personal history of MDD were found to have decreased resting-state functional connectivity (RSFC) between the lateral parietal cortex (LPC) and the posterior cingulate cortex (PCC), and their Edinburgh Postnatal Depression Scale (EPDS) scores were significantly higher shortly after childbirth. More often than not, these women also had a family history of MDD. While women with no history of depression showed a negative association between hair cortisol concentration (HCC) and gray matter volume (GMV) in the medial orbitofrontal cortex (mOFC), the opposite trend was seen in women with a history of depression. This implies that women with remitted depression show distinctive neural phenotypes with subclinical residual symptoms, which likely predispose them to later depressive episodes. • A history of depression is one of the main risk factors for a new depressive episode • Shortly after birth, women with a history of depression show higher depression scores • Women with a history of depression show reduced RSFC between the LPC and the PCC • The relationship between HCC and GMV of mOFC varies depending on previous depression [ABSTRACT FROM AUTHOR]

Multimodal neuroimaging studies combining proton magnetic resonance spectroscopy (1H-MRS) to quantify GABA and/or glutamate concentrations and functional magnetic resonance imaging (fMRI) to measure brain activity non-invasively have advanced understanding of how neurochemistry and neurophysiology may be related at a macroscopic level. The present study aimed to perform a systematic review and meta-analysis of available studies examining the relationship between 1H-MRS glutamate and/or GABA levels and task-related fMRI signal in the healthy brain. Ovid (Medline, Embase, and PsycINFO) and Pubmed databases were systematically searched to identify articles published until December 2019. The primary outcome of interest was the association between resting levels of glutamate or GABA and task-related fMRI. Fifty-five papers were identified for inclusion in the systematic review. A further 22 studies were entered into four separate meta-analyses. These meta-analyses found evidence of significant negative associations between local GABA levels and (a) fMRI activation to visual tasks in the occipital lobe, and (b) activation to emotion processing in the medial prefrontal cortex (mPFC)/anterior cingulate cortex (ACC). However, there was no significant association between mPFC/ACC glutamate levels and fMRI activation to cognitive control tasks or to emotional processing, with the relationship to emotion processing related neural activity narrowly missing significance. Moreover, our systematic review also found converging evidence of negative associations between GABA levels and local brain activity, and positive associations between glutamate levels and distal brain activity, outside of the 1H-MRS sampling region. Albeit less consistently, additional relationships between GABA levels and distal brain activity and between glutamate levels and local brain activity were found. It remains unclear if the absence of effects for other brain regions and other cognitive-emotional domains reflects study heterogeneity or potential confounding effects of age, sex, or other unknown factors. Advances in 1H-MRS methodology as well as in the integration of 1H-MRS readouts with other imaging modalities for indexing neural activity hold great potential to reveal key aspects of the pathophysiology of mental health disorders involving aberrant interactions between neurochemistry and neurophysiology such as schizophrenia. [ABSTRACT FROM AUTHOR]

Within-individual coupling between measures of brain structure and function evolves in development and may underlie differential risk for neuropsychiatric disorders. Despite increasing interest in the development of structure-function relationships, rigorous methods to quantify and test individual differences in coupling remain nascent. In this article, we explore and address gaps in approaches for testing and spatially localizing individual differences in intermodal coupling. We propose a new method, called CIDeR, which is designed to simultaneously perform hypothesis testing in a way that limits false positive results and improve detection of true positive results. Through a comparison across different approaches to testing individual differences in intermodal coupling, we delineate subtle differences in the hypotheses they test, which may ultimately lead researchers to arrive at different results. Finally, we illustrate the utility of CIDeR in two applications to brain development using data from the Philadelphia Neurodevelopmental Cohort., Competing Interests: Declaration of competing interest AAB receives consulting income from Octave Bioscience and holds equity in Centile Bioscience.

Synaptic terminal density is thought to influence cognitive function and neural activity, yet its role in cognition has not been explored in healthy humans. We examined these relationships using [11C]UCB-J positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) in 25 healthy adults performing cognitive function tasks in the scanner. We found a significant positive association between synaptic terminal density, indicated by [11C]UCB-J PET distribution volume ratio (DVRcs), and neural activity during task switching (PLS-CA, second canonical component, r = 0.63, p = 0.043) with the thalamus-putamen data positively contributing to this relationship (PLS-CA loading 0.679, exploratory Pearson's correlation r = 0.42, p = 0.044, uncorrected). Furthermore, synaptic terminal density predicted switch cost (PLS-R, R 2 = 0.45, RMSE = 0.06, p = 0.022), with DVRcs negatively correlating with switch cost in key brain regions including the dorsolateral prefrontal cortex and posterior frontal cortex. Conversely, no significant relationships were observed between [11C]UCB-J DVRcs and neural activity or performance measures in the N-back working memory task, suggesting interindividual differences in synaptic terminal density may be more closely related to some cognitive functions and not others., Competing Interests: Dr. Howes has received investigator-initiated research funding from and/or participated in advisory/speaker meetings organised by Angellini, Autifony, Biogen, Boehringer-Ingelheim, Eli Lilly, Elysium, Heptares, Global Medical Education, Invicro, Jansenn, Karuna, Lundbeck, Merck, Neurocrine, Ontrack/ Pangea, Otsuka, Sunovion, Recordati, Roche, Rovi and Viatris/ Mylan. He was previously a part-time employee of Lundbeck A/v. Neither Dr. Howes nor his family have holdings/a financial stake in any pharmaceutical company. Dr. Howes has a patent for the use of dopaminergic imaging. Ilan Rabiner, Matt Wall, Alexander Whittington and Ayla Mansur are all employees or past employees of Invicro London. Tiago Reis Marques is an employee and founder of Pasithea Therapeutics. Other authors have reported no biomedical financial interests or potential conflicts of interest., (© 2024 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.)

Background: Exercise dependence (ED) is characterised by behavioural and psychological symptoms that resemble those of substance use disorders. However, it remains inconclusive whether ED is accompanied by similar brain alterations as seen in substance use disorders. Therefore, we investigated brain alterations in individuals with ED and inactive control participants., Methods: In this cross-sectional neuroimaging investigation, 29 individuals with ED as assessed with the Exercise Dependence Scale (EDS) and 28 inactive control participants (max one hour exercising per week) underwent structural and functional resting-state magnetic resonance imaging (MRI). Group differences were explored using voxel-based morphometry and functional connectivity analyses. Analyses were restricted to the striatum, amygdala, and inferior frontal gyrus (IFG). Exploratory analyses tested whether relationships between brain structure and function were differently related to EDS subscales among groups., Results: No structural differences were found between the two groups. However, right IFG and bilateral putamen volumes were differently related to the EDS subscales "time" and "tolerance", respectively, between the two groups. Resting-state functional connectivity was increased from right IFG to right superior parietal lobule in individuals with ED compared to inactive control participants. Furthermore, functional connectivity of the angular gyrus to the left IFG and bilateral caudate showed divergent relationships to the EDS subscale "tolerance" among groups., Discussion: The findings suggest that ED may be accompanied by alterations in cognition-related brain structures, but also functional changes that may drive compulsive habitual behaviour. Further prospective studies are needed to disentangle beneficial and detrimental brain effects of ED.

The motivation to approach or avoid incentives can change during adolescence. Advances in neuroimaging allow researchers to characterize specific brain circuits that underlie these developmental changes. Whereas activity in the nucleus accumbens (NAcc) can predict approach toward incentive gain, activity in anterior insula (AIns) is associated with avoidance of incentive loss. Recent research characterized the structural white-matter tract connecting the two brain regions, but the tract has neither been characterized in adolescence nor linked to functional activity during incentive anticipation. In this study, we collected diffusion MRI and characterized the tract connecting the AIns to the NAcc for the first time in early adolescents. We then measured NAcc functional activity during a monetary incentive delay task and found that structural coherence of the AIns-NAcc tract is correlated with decreased functional activity at the NAcc terminal of the tract during anticipation of no incentives. In adolescents who completed an assessment 2 years later, we found that AIns-NAcc tract coherence could predict greater future self-reported motivation, and that NAcc functional activity could statistically mediate this association. Together, the findings establish links from brain structure to function to future motivation and provide targets to study the reciprocal development of brain structure and function.

Alzheimer's disease (AD) will become a global burden in the coming decades according to the latest statistical survey. How to effectively detect AD or MCI (mild cognitive impairment) using reliable biomarkers and robust machine learning methods has become a challenging problem. In this study, we propose a novel AD multiclass classification framework with embedding feature selection and fusion based on multimodal neuroimaging. The framework has three novel aspects: (1) An l 2 , 1 -norm regularization term combined with the multiclass hinge loss is used to naturally select features across all the classes in each modality. (2) To fuse the complementary information contained in each modality, an l p -norm (1 < p < ∞) regularization term is introduced to combine different kernels to perform multiple kernel learning to avoid a sparse kernel coefficient distribution, thereby effectively exploiting complementary modalities. (3) A theorem that transforms the multiclass hinge loss minimization problem using the l 2 , 1 -norm and l p -norm regularizations to a previous solvable optimization problem and its proof are given. Additionally, it is theoretically proved that the optimization process converges to the global optimum. Extensive comparison experiments and analysis support the promising performance of the proposed method. • Al 2,1 -norm term is used to yield structural sparsity to select features. • Al p -norm term is used to exploit complementary information among multimodalities. • A theorem and its proof are given to guide the optimization of the proposed method. [ABSTRACT FROM AUTHOR]

Objective: To build a model to predict cognitive status reflecting structural, functional, and white matter integrity changes in early multiple sclerosis (MS). Methods: Based on Symbol Digit Modalities Test (SDMT) performance, 183 early MS patients were assigned "lower" or "higher" performance groups. Three-dimensional (3D)-T2, T1, diffusion weighted, and resting-state magnetic resonance imaging (MRI) data were acquired in 3T. Using Random Forest, five models were trained to classify patients into two groups based on 1—demographic/clinical, 2—lesion volume/location, 3—local/global tissue volume, 4—local/global diffusion tensor imaging, and 5—whole-brain resting-state-functional-connectivity measures. In a final model, all important features from previous models were concatenated. Area under the receiver operating characteristic curve (AUC) values were calculated to evaluate classifier performance. Results: The highest AUC value (0.90) was achieved by concatenating all important features from neuroimaging models. The top 10 contributing variables included volumes of bilateral nucleus accumbens and right thalamus, mean diffusivity of left cingulum-angular bundle, and functional connectivity among hubs of seven large-scale networks. Conclusion: These results provide an indication of a non-random brain pattern mostly compromising areas involved in attentional processes specific to patients who perform worse in SDMT. High accuracy of the final model supports this pattern as a potential neuroimaging biomarker of subtle cognitive changes in early MS. [ABSTRACT FROM AUTHOR]

Recently multimodal neuroimaging which combines signals from different brain modalities has started to be considered as a potential to improve the accuracy of diagnosis. The current study aimed to explore a new method for discriminating attention-deficit hyperactivity disorder (ADHD) patients and control group by means of simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Twenty-three pre-medicated combined type ADHD children and 21 healthy children were included in the study. Nonlinear brain dynamics of subjects were obtained from EEG signal using Higuchi fractal dimensions and Lempel–Ziv complexity, latency and amplitude values of P3 wave obtained from auditory evoked potentials and frontal cortex hemodynamic responses calculated from fNIRS. Lower complexity values, prolonged P3 latency and reduced P3 amplitude values were found in ADHD children. fNIRS indicated that the control subjects exhibited higher right prefrontal activation than ADHD children. Features are analyzed, looking for the best classification accuracy and finally machine learning techniques, namely Support Vector Machines, Naïve Bayes and Multilayer Perception Neural Network, are introduced for EEG signals alone and for combination of fNIRS and EEG signals. Naive Bayes provided the best classification with an accuracy rate of 79.54% and 93.18%, using EEG and EEG-fNIRS systems, respectively. Our findings demonstrate that utilization of information by combining features obtained from fNIRS and EEG improves the classification accuracy. As a conclusion, our method has indicated that EEG-fNIRS multimodal neuroimaging is a promising method for ADHD objective diagnosis. [ABSTRACT FROM AUTHOR]

Non-human primates (NHPs) are vital models for neuroscience research. These animals have been widely used in behavioral, electrophysiological, molecular, and more recently, multimodal neuroimaging and neuro-engineering studies. Several RF coil arrays have been designed for functional, high-resolution brain magnetic resonance imaging (MRI), but few have been designed to accommodate multimodal devices. In the present study, a 16-channel array coil was constructed for brain imaging of macaques at 3 Tesla (3 T). To construct this coil, a close-fitting helmet-shaped form was designed to host 16 coil loops for whole-brain coverage. This assembly is mountable onto stereotaxic head frame bars, and the coil functions while the monkey is in the sphinx position with a clear line of vision of stimuli presented from outside of the MRI system. In addition, 4 openings were allocated in the coil housing, allowing multimodal devices to directly access visual cortical regions such as V1-V4 and MT. Coil performance was evaluated in an anesthetized macaque by quantifying and comparing signal-to-noise ratios (SNRs), noise correlations, and g-factor maps to a vendor-supplied human pediatric coil frequently used for NHP MRI. The result from in vivo experiments showed that the NHP coil was well-decoupled, had higher SNRs in cortical regions, and improved data acquisition acceleration capability compared with a vendor-supplied human pediatric coil that has been frequently used in macaque MRI studies. Furthermore, whole-brain anatomic imaging, diffusion tensor imaging and functional brain imaging have also been conducted: the details of brain anatomical structure, such as cerebellum and brainstem, can be clearly visualized in T2-SPACE images; b0 SNR calculated from b0 maps was higher than the human pediatric coil in all regions of interest (ROIs); the time-course SNR (tSNR) map calculated for GRE-EPI images demonstrates that the presented coil can be used for high-resolution functional imaging at 3 T. [ABSTRACT FROM AUTHOR]