Search

AIMS AND BACKGROUND:\ud Dementia is diagnosed through a combination of clinical assessment, cognitive assessment tools and neuroimaging. The aim of this retrospective, naturalistic study was to explore the association between the clinical assessment tools used in a memory clinic and the findings of Magnetic Resonance Imaging (MRI) scans in patients with dementia.\ud \ud METHODS:\ud Data were collected through routine clinical practice for all patients assessed at a memory assessment clinic in East Sussex, UK. Included patients had an MRI scan and received a formal diagnosis of dementia. Multinomial logistic regression was used to investigate the associations between atrophy on MRI with age, gender, Cambridge Cognitive Examination (CAMCOG) and Hachinski Ischemic Score (HIS). Ordinal logistic regression was used to study the associations between vascular findings on MRI with age, gender, CAMCOG and HIS. Because of the distribution of HIS scores a cut-off of 1 or greater was used in the regression analysis.\ud \ud RESULTS:\ud Male gender was associated with an increased likelihood of moderate atrophy (relative risk ratio (RRR) = 1.99, 95% confidence interval (CI) = 1.04-3.82), severe atrophy (RRR = 3.04, 95% CI = 1.38-6.68) and regional atrophy (RRR = 2.25, 95% CI = 1.26-4.00) on MRI. An increase of one point on the CAMCOG was associated with a decreased risk of regional atrophy (RRR = 0.98, 95% CI = 0.96-1.00) on MRI. There were no significant associations between age, or HIS, and atrophy on MRI. An increase in age of one year was associated with an increase in severity of vascular pathology reported on MRI (OR = 1.08, 95% CI = 1.05-1.12). Male gender was associated with reduced severity of vascular pathology reported on MRI (OR = 0.53, 95% CI = 0.36-0.78). There were no associations between CAMCOG, or HIS, and vascular pathology on MRI.\ud \ud DISCUSSION:\ud Our data show that CAMCOG was associated with MRI findings of regional atrophy and vascular pathology was greater in older patients. We highlight the importance of using a multi-modal approach to dementia diagnosis.

Autism spectrum disorder is a neurodevelopmental disorder characterized by core deficits in social functioning. Core autistics traits refer to poor social and imagination skills, poor attention-switching/strong focus of attention, exceptional attention to detail, as expressed by the autism-spectrum quotient. Over the years, the importance of the cerebellum in the aetiology of autism spectrum disorder has been acknowledged. Neuroimaging studies have provided a strong support to this view, showing both structural and functional connectivity alterations to affect the cerebellum in autism spectrum disorder. According to the underconnectivity theory, disrupted connectivity within cerebello-cerebral networks has been specifically implicated in the aetiology of autism spectrum disorder. However, inconsistent results have been generated across studies. In this study, an integrated approach has been used in a selected population of adults with autism spectrum disorder to analyse both cerebellar morphometry and functional connectivity. In individuals with autism spectrum disorder, a decreased cerebellar grey matter volume affected the right Crus II, a region showing extensive connections with cerebral areas related to social functions. This grey matter reduction correlates with the degree of autistic traits as measured by autism-spectrum quotient. Interestingly, altered functional connectivity was found between the reduced cerebellar Crus II and contralateral cerebral regions, such as frontal and temporal areas. Overall, the present data suggest that adults with autism spectrum disorder present with specific cerebellar structural alterations that may affect functional connectivity within cerebello-cerebral modules relevant to social processing and account for core autistics traits.

\ud Background: \ud Amnestic mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer’s disease (AD). However, the clinical conversion from MCI to AD is unpredictable. Hence, identification of non-invasive biomarkers able to detect early changes induced by dementia is a pressing need.\ud \ud Purpose: \ud To explore the added value of histogram analysis applied to measures derived from diffusion tensor imaging (DTI) for detecting brain tissue differences between AD, MCI and healthy subjects (HS).\ud \ud Study type: \ud Retrospective.\ud \ud Population/subjects: \ud Local cohort (57 AD, 28 MCI, 23 HS), Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort (41 AD, 58 MCI, 41 HS).\ud \ud Field Strength: \ud 3T. Dual echo TSE; FLAIR; MDEFT; IR-SPGR; DTI.\ud \ud Assessment: \ud Normal appearing white matter (NAWM) masks were obtained using the T1-weighted volumes for tissue segmentation and T2-weighted images for removal of hyperintensities/lesions. From DTI images, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AXD) and radial diffusivity (RD) were obtained. NAWM histograms of FA, MD, AXD and RD were derived and characterized estimating: peak height, peak location, mean value (MV), and quartiles (C25, C50, C75), which were compared between groups. Receiver operating characteristic (ROC) and area under ROC curves (AUC) were\ud calculated. To confirm our results, the same analysis was repeated on ADNI dataset.\ud \ud Statistical tests: \ud One-way ANOVA, post-hoc Student’s t-test, multi-class ROC analysis.\ud \ud Results: \ud For the local cohort, C25 of AXD had the maximum capability of group discrimination with AUC of 0.80 for “HS vs patients” comparison and 0.74 for “AD vs others” comparison. For ADNI cohort, MV of AXD revealed the maximum group discrimination capability with AUC of 0.75 for “HS vs patients” comparison and 0.75 for “AD vs others” comparison.\ud \ud Data conclusion: \ud AXD of NAWM might be an early marker of microstructural brain tissue changes occurring during AD course and might be useful for assessing disease progression.

Purpose\ud\udTo develop a framework to fully characterize quantitative magnetization transfer indices in the human cervical cord in vivo within a clinically feasible time.\udMethods\ud\udA dedicated spinal cord imaging protocol for quantitative magnetization transfer was developed using a reduced field‐of‐view approach with echo planar imaging (EPI) readout. Sequence parameters were optimized based in the Cramer‐Rao‐lower bound. Quantitative model parameters (i.e., bound pool fraction, free and bound pool transverse relaxation times [ urn:x-wiley:07403194:media:mrm26909:mrm26909-math-0014, urn:x-wiley:07403194:media:mrm26909:mrm26909-math-0015], and forward exchange rate [kFB]) were estimated implementing a numerical model capable of dealing with the novelties of the sequence adopted. The framework was tested on five healthy subjects.\udResults\ud\udCramer‐Rao‐lower bound minimization produces optimal sampling schemes without requiring the establishment of a steady‐state MT effect. The proposed framework allows quantitative voxel‐wise estimation of model parameters at the resolution typically used for spinal cord imaging (i.e. 0.75 × 0.75 × 5 mm3), with a protocol duration of ∼35 min. Quantitative magnetization transfer parametric maps agree with literature values. Whole‐cord mean values are: bound pool fraction = 0.11(±0.01), urn:x-wiley:07403194:media:mrm26909:mrm26909-math-0016 = 46.5(±1.6) ms, urn:x-wiley:07403194:media:mrm26909:mrm26909-math-0017 = 11.0(±0.2) µs, and kFB = 1.95(±0.06) Hz. Protocol optimization has a beneficial effect on reproducibility, especially for urn:x-wiley:07403194:media:mrm26909:mrm26909-math-0018 and kFB.\udConclusion\ud\udThe framework developed enables robust characterization of spinal cord microstructure in vivo using qMT. Magn Reson Med 79:2576–2588, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The frontal aslant tract (FAT) has been described as a bundle connecting the Broca’s area to the supplementary motor area (SMA) and the pre-SMA in both hemispheres. The functional properties of this tract and its role in degenerative dementia, such as Alzheimer’s disease (AD), still need to be fully clarified. The aim of this study was to explore the microstructural integrity of the FAT in patients with AD and its potential relationship with cognitive functioning. Twenty-three patients with AD and 25 healthy subjects (HS) were enrolled. All subjects underwent cognitive and MRI examination. MRI, including diffusion sequences, was used for probabilistic tractography analysis. We reconstructed individual FATs bilaterally and assessed their microstructural integrity using fractional anisotropy (FA), computed as both mean tract value and voxel-wise using SPM-8. Mean FA values were then used to test for correlations with cognitive measures. Mean tract FA and voxel-wise analyses revealed that patients with AD, compared to HS, had decreased FA in the FAT bilaterally. In addition, positive associations were found between FA in the FATs and patients’ performance at tests for constructional praxis and visuospatial logical reasoning. The present results reveal a bilateral damage of FAT in AD patients. The association between FATs’ microscopic abnormalities and constructive abilities fits well with the knowledge of a functional involvement of SMA and pre-SMA in movement sequences when executing constructive praxis tasks. The FAT is an associative bundle critically involved in the network sub-serving constructional praxis in patients with AD.

The MRI signal is dependent upon a number of sub-voxel properties of tissue, which makes it potentially able to detect changes occurring at a scale much smaller than the image resolution. This "microstructural imaging" has become one of the main branches of quantitative MRI. Despite the exciting promise of unique insight beyond the resolution of the acquired images, its widespread application is limited by the relatively modest ability of each microstructural imaging technique to distinguish between differing microscopic substrates. This is mainly due to the fact that MRI provides a very indirect measure of the tissue properties in which we are interested. A strategy to overcome this limitation lies in the combination of more than one technique, to exploit the relative contributions of differing physiological and pathological substrates to selected MRI contrasts. This forms the basis of multi-modal MRI, a broad concept that refers to many different ways of effectively combining information from more than one MRI contrast. This paper will review a range of methods that have been proposed to maximise the output of this combination, primarily falling into one of two approaches. The first one relies on data-driven methods, exploiting multivariate analysis tools able to capture overlapping and complementary information. The second approach, which we call "model-driven", aims at combining parameters extracted by existing biophysical or signal models to obtain new parameters, which are believed to be more accurate or more specific than the original ones. This paper will attempt to provide an overview of the advantages and limitations of these two philosophies.

Changes in the residual memory variance are considered as a dynamic aspect of cognitive reserve (d-CR). We aimed to investigate for the first time the neural substrate associated with changes in the residual memory variance overtime in patients with amnestic mild cognitive impairment (aMCI). Thirty-four aMCI patients followed-up for 36 months and 48 healthy elderly individuals (HE) were recruited. All participants underwent 3T MRI, collecting T1-weighted images for voxel-based morphometry (VBM). They underwent an extensive neuropsychological battery, including six episodic memory tests. In patients and controls, factor analyses were used on the episodic memory scores to obtain a composite memory score (C-MS). Partial Least Square analyses were used to decompose the variance of C-MS in latent variables (LT scores), accounting for demographic variables and for the general cognitive efficiency level; linear regressions were applied on LT scores, striping off any contribution of general cognitive abilities, to obtain the residual value of memory variance, considered as an index of d-CR. LT scores and d-CR were used in discriminant analysis, in patients only. Finally, LT scores and d-CR were used as variable of interest in VBM analysis. The d-CR score was not able to correctly classify patients. In both aMCI patients and HE, LT1st and d-CR scores showed correlations with grey matter volumes in common and in specific brain areas. Using CR measures limited to assess memory function is likely less sensitive to detect the cognitive decline and predict the evolution of Alzheimer's disease. In conclusion, d-CR needs a measure of general cognition to identify conversion to Alzheimer's disease efficiently.

Artifact removal in resting state fMRI (rfMRI) data remains a serious challenge, with even subtle head motion undermining reliability and reproducibility. Here we compared some of the most popular single-echo de-noising methodsÐregression of Motion parameters, White matter and Cerebrospinal fluid signals (MWC method), FMRIB's ICA-based X-noiseifier (FIX) and ICA-based Automatic Removal Of motion Artifacts (ICA-AROMA)Ðwith a multiecho approach (ME-ICA) that exploits the linear dependency of BOLD on the echo time. Data were acquired using a clinical scanner and included 30 young, healthy participants (minimal head motion) and 30 Attention Deficit Hyperactivity Disorder patients (greater head motion). De-noising effectiveness was assessed in terms of data quality after each cleanup\ud procedure, ability to uncouple BOLD signal and motion and preservation of default mode network (DMN) functional connectivity. Most cleaning methods showed a positive impact on data quality. However, based on the investigated metrics, ME-ICA was the most robust. It minimized the impact of motion on FC even for high motion participants and preserved DMN functional connectivity structure. The high-quality results obtained using ME-ICA suggest that using a multi-echo EPI sequence, reliable rfMRI data can be obtained in a clinical setting.

BACKGROUND\ud \ud James and Lange proposed that emotions are the perception of physiological reactions. Two-level theories of emotion extend this model to suggest that cognitive interpretations of physiological changes shape self-reported emotions. Correspondingly false physiological feedback of evoked or tonic bodily responses can alter emotional attributions. Moreover, anxiety states are proposed to arise from detection of mismatch between actual and anticipated states of physiological arousal. However, the neural underpinnings of these phenomena previously have not been examined.\ud \ud METHODOLOGY/PRINCIPAL FINDINGS\ud \ud We undertook a functional brain imaging (fMRI) experiment to investigate how both primary and second-order levels of physiological (viscerosensory) representation impact on the processing of external emotional cues. 12 participants were scanned while judging face stimuli during both exercise and non-exercise conditions in the context of true and false auditory feedback of tonic heart rate. We observed that the perceived emotional intensity/salience of neutral faces was enhanced by false feedback of increased heart rate. Regional changes in neural activity corresponding to this behavioural interaction were observed within included right anterior insula, bilateral mid insula, and amygdala. In addition, right anterior insula activity was enhanced during by asynchronous relative to synchronous cardiac feedback even with no change in perceived or actual heart rate suggesting this region serves as a comparator to detect physiological mismatches. Finally, BOLD activity within right anterior insula and amygdala predicted the corresponding changes in perceived intensity ratings at both a group and an individual level.\ud \ud CONCLUSIONS/SIGNIFICANCE\ud \ud Our findings identify the neural substrates supporting behavioural effects of false physiological feedback, and highlight mechanisms that underlie subjective anxiety states, including the importance of the right anterior insula in guiding second-order "cognitive" representations of bodily arousal state.