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We describe brain structural damage and cognitive profile evolution of an adult patient with 17q21.31 microduplication, a rare condition associated with psychomotor delay, behavioural disturbances and poor social interaction.A.B., 57 years old, male, displayed obsessive and repetitive behaviours, irritability, scarce hygiene and memory loss at disease onset. He had strong familiarity for adult-onset behavioural alterations (his father and sister) and neuropsychiatric conditions (his son). Blood and cerebrospinal fluid (CSF) samples revealed 17q21.31 microduplication, shared also by his son and sister, and raised CSF tau, respectively. He was hospitalized 1 year after disease onset and underwent an MRI scan and a neuropsychological assessment, the latter being repeated 7 months later. To quantitatively investigate patient's grey matter (GM) volume, 16 age- and education-matched male controls were selected and voxel-based morphometry analysis was performed.During hospitalization, his behavioural profile was characterized by anosognosia, impulsivity, apathy and aggressiveness. Cognitive testing revealed main attentive-executive disturbances and difficulties in understanding non-literal language. Compared to controls, A.B. had greater GM atrophy mainly in the right hemisphere, involving amygdala, hippocampus, inferior/superior temporal gyri and temporal pole. He received a diagnosis of early onset dementia. After 7 months, he developed empathy loss, perseverative behaviour, changes in eating habits and worsening in executive-attentive abilities.In A.B., 17q21.31 microduplication caused a neurodegenerative condition with prevalent right temporal damage, raised CSF tau level, behavioural disturbances, memory impairment, attentive-executive and abstract language dysfunctions and fast disease progression, thus reflecting the complex interaction between such genetic substrate and clinical phenotypes.

Background and objectives: To explore cognitive, EEG, and MRI features in COVID-19 survivors up to 10months after hospital discharge. Methods: Adult patients with a recent diagnosis of COVID-19 and reporting subsequent cognitive complaints underwent neuropsychological assessment and 19-channel-EEG within 2months (baseline, N = 49) and 10months (follow-up, N = 33) after hospital discharge. A brain MRI was obtained for 36 patients at baseline. Matched healthy controls were included. Using eLORETA, EEG regional current densities and linear lagged connectivity values were estimated. Total brain and white matter hyperintensities (WMH) volumes were measured. Clinical and instrumental data were evaluated between patients and controls at baseline, and within patient whole group and with/without dysgeusia/hyposmia subgroups over time. Correlations among findings at each timepoint were computed. Results: At baseline, 53% and 28% of patients showed cognitive and psychopathological disturbances, respectively, with executive dysfunctions correlating with acute-phase respiratory distress. Compared to healthy controls, patients also showed higher regional current density and connectivity at delta band, correlating with executive performances, and greater WMH load, correlating with verbal memory deficits. A reduction of cognitive impairment and delta band EEG connectivity were observed over time, while psychopathological symptoms persisted. Patients with acute dysgeusia/hyposmia showed lower improvement at memory tests than those without. Lower EEG delta band at baseline predicted worse cognitive functioning at follow-up. Discussion: COVID-19 patients showed interrelated cognitive, EEG, and MRI abnormalities 2months after hospital discharge. Cognitive and EEG findings improved at 10months. Dysgeusia and hyposmia during acute COVID-19 were related with increased vulnerability in memory functions over time.

Background and ObjectivesMRI connectomics is an ideal tool to test a network-based model of pathologic propagation from a disease epicenter in neurodegenerative disorders. In this study, we used a novel graph theory-based MRI paradigm to explore functional connectivity reorganization, discerning between direct and indirect connections from disease epicenters, and its relationship with neurodegeneration across clinical presentations of the frontotemporal dementia (FTD) spectrum, including behavioral variant of FTD (bvFTD), nonfluent variant of primary progressive aphasia (nfvPPA), and semantic variant of primary progressive aphasia (svPPA).MethodsIn this observational cross-sectional study, disease epicenters were defined as the peaks of atrophy of a cohort of patients with high confidence of frontotemporal lobar degeneration pathology (Mayo Clinic). These were used as seed regions for stepwise functional connectivity (SFC) analyses in an independent (Milan) set of patients with FTD to assess connectivity in regions directly and indirectly connected to the epicenters. Correlations between SFC architecture in healthy conditions and atrophy patterns in patients with FTD were also tested.ResultsAs defined by comparing the 42 Mayo Clinic patients with 15 controls, disease epicenters were the left anterior insula for bvFTD, left supplementary motor area for nfvPPA, and left inferior temporal gyrus (ITG) for svPPA. Compared with 94 age-matched controls, patients with bvFTD (n = 64) and nfvPPA (n = 34) of the Milan cohort showed widespread decreased SFC in bilateral cortical regions with direct/indirect connections with epicenters and increased SFC either in directly connected regions, physically close to the respective seed region, or in more distant cortical/cerebellar areas with indirect connections. Across all link steps, svPPA (n = 36) showed SFC decrease mostly within the temporal lobes, with co-occurrent SFC increase in cerebellar regions at indirect link steps. The average stepwise topological distance from the left ITG in a reference group of 50 young healthy controls correlated with regional gray matter volume in svPPA, consistent with network-based degeneration.DiscussionOur findings demonstrate that each FTD syndrome is associated with a characteristic interplay of decreased and increased functional connectivity with the disease epicenter, affecting both direct and indirect connections. SFC revealed novel insights regarding the topology of functional disconnection across FTD syndromes, holding the promise to be used to model disease progression in future longitudinal studies.

Neuroimaging studies point to neurostructural abnormalities in youth with anxiety disorders. Yet, findings are based on small-scale studies, often with small effect sizes, and have limited generalizability and clinical relevance. These issues have prompted a paradigm shift in the field towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Here, we built and validated neurostructural machine learning (ML) models for individual-level inferences based on the largest-ever multi-site neuroimaging sample of youth with anxiety disorders (age: 10-25 years, N=3,343 individuals from 32 global sites), as compiled by three ENIGMA Anxiety Working Groups: Panic Disorder (PD), Generalized Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). ML classifiers were trained on MRI-derived regional measures of cortical thickness, surface area, and subcortical volumes to classify patients and healthy controls (HC) for each anxiety disorder separately and across disorders (transdiagnostic classification). Modest, yet robust, classification performance was achieved for PD vs. HC (AUC=0.62), but other disorder-specific and transdiagnostic classifications were not significantly different from chance. However, above chance-level transdiagnostic classifications were obtained in exploratory subgroup analyses of male patients vs. male HC, unmedicated patients vs. HC, and patients with low anxiety severity vs. HC (AUC 0.59-0.63). The above chance-level classifications were based on plausible and specific neuroanatomical features in fronto-striato-limbic and temporo-parietal regions. This study provides a realistic estimate of classification performance in a large, ecologically valid, multi-site sample of youth with anxiety disorders, and may as such serve as a benchmark.

Objective: Detecting and monitoring cognitive and behavioral deficits in motor neuron diseases (MND) is critical due to their considerable clinical impact. In this scenario, computer-based batteries may play an important role. In this study, we investigated the progression of cognitive and behavioral deficits in MND patients using both standard and computer-based neuropsychological batteries. Methods: This is a retrospective study on 74 MND patients (52 amyotrophic lateral sclerosis [ALS], 12 primary lateral sclerosis [PLS], and 10 progressive muscular atrophy [PMA]) who were followed up for 12 months and underwent up to three cognitive/behavioral assessments, 6 months apart, including standard and/or computerized based (the Test of Attentional Performance [TAP]) batteries. Behavioral/cognitive changes were investigated over time using generalized linear model for longitudinal data accounting for time and revised-ALS Functional Rating Scale. Results: Over 12 months, ALS patients showed a global cognitive decline (Mini Mental State Examination) at the standard battery and reduced performance in the alertness, sustained and divided attention, go/nogo, cross-modal and incompatibility TAP tasks. Most of these findings remained significant when ALSFRS-R changes over time were included as covariate in the analyses. ALS patients did not show significant behavioral abnormalities over time. No cognitive and behavioral changes were found in PLS and PMA cases. Conclusions: Computer-based neuropsychological evaluations are able to identify subtle cognitive changes in ALS, unique to this condition. This study highlights the need of specific, accurate and well-tolerated tools for the monitoring of cognitive deficits in MND.

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.

Background and ObjectivesA significant overlap between amyotrophic lateral sclerosis (ALS) and behavioral variant of frontotemporal dementia (bvFTD) has been observed at clinical, genetic, and pathologic levels. Within this continuum of presentations, the presence of mild cognitive or behavioral symptoms in patients with ALS has been consistently reported, although it is unclear whether this is to be considered a distinct phenotype or rather a natural evolution of ALS. Here, we used mathematical modeling of MRI connectomic data to decipher common and divergent neural correlates across the ALS–frontotemporal dementia (FTD) spectrum.MethodsWe included 83 patients with ALS, 35 patients with bvFTD, and 61 healthy controls, who underwent clinical, cognitive, and MRI assessments. Patients with ALS were classified according to the revised Strong criteria into 54 ALS with only motor deficits (ALS-cn), 21 ALS with cognitive or behavioral involvement (ALS-ci/bi), and 8 ALS with bvFTD (ALS-FTD). First, we assessed the functional and structural connectivity patterns across the ALS-FTD spectrum. Second, we investigated whether and where MRI connectivity alterations of patients with ALS with any degree of cognitive impairment (i.e., ALS-ci/bi and ALS-FTD) resembled more the pattern of damage of one (ALS-cn) or the other end (bvFTD) of the spectrum, moving from group-level to single-subject analysis.ResultsAs compared with controls, extensive structural and functional disruption of the frontotemporal and parietal networks characterized bvFTD (bvFTD-like pattern), while a more focal structural damage within the sensorimotor-basal ganglia areas characterized ALS-cn (ALS-cn-like pattern). ALS-ci/bi patients demonstrated an ALS-cn-like pattern of structural damage, diverging from ALS-cn with similar motor impairment for the presence of enhanced functional connectivity within sensorimotor areas and decreased functional connectivity within the bvFTD-like pattern. On the other hand, patients with ALS-FTD resembled both structurally and functionally the bvFTD-like pattern of damage with, in addition, the structural ALS-cn-like damage in the motor areas.DiscussionOur findings suggest a maladaptive role of functional rearrangements in ALS-ci/bi concomitantly with similar structural alterations compared to ALS-cn, supporting the hypothesis that ALS-ci/bi might be considered as a phenotypic variant of ALS, rather than a consequence of disease worsening.

This study aimed to identify functional neuroimaging patterns anticipating the clinical indication for deep brain stimulation (DBS) in patients with Parkinson’s disease (PD). A cohort of prospectively recruited patients with PD underwent neurological evaluations and resting-state functional MRI (RS-fMRI) at baseline and annually for 4 years. Patients were divided into two groups: 19 patients eligible for DBS over the follow-up and 41 patients who did not meet the criteria to undergo DBS. Patients selected as candidates for DBS did not undergo surgery at this stage. Sixty age- and sex-matched healthy controls performed baseline evaluations. Graph analysis and connectomics assessed global and local topological network properties and regional functional connectivity at baseline and at each time point. At baseline, network analysis showed a higher mean nodal strength, local efficiency, and clustering coefficient of the occipital areas in candidates for DBS over time relative to controls and patients not eligible for DBS. The occipital hyperconnectivity pattern was confirmed by regional analysis. At baseline, a decreased functional connectivity between basal ganglia and sensorimotor/frontal networks was found in candidates for DBS compared to patients not eligible for surgery. In the longitudinal analysis, patient candidate for DBS showed a progressively decreased topological brain organization and functional connectivity, mainly in the posterior brain networks, and a progressively increased connectivity of basal ganglia network compared to non-candidates for DBS. RS-fMRI may support the clinical indication to DBS and could be useful in predicting which patients would be eligible for DBS in the earlier stages of PD.

Highlights • Theta density increase is the earliest and most sensitive EEG marker of AD pathology. • Alpha2 density progressively decreases following the progression of AD pathology. • EEG graph analysis of ADD patients shows network derangement at theta and alpha2 band. • EEG/fMRI integration model empowered EEG diagnostic performances., Objective We evaluated the value of resting-state EEG source biomarkers to characterize mild cognitive impairment (MCI) subjects with an Alzheimer’s disease (AD)-like cerebrospinal fluid (CSF) profile and to track neurodegeneration throughout the AD continuum. We further applied a resting-state functional MRI (fMRI)-driven model of source reconstruction and tested its advantage in terms of AD diagnostic accuracy. Methods Thirty-nine consecutive patients with AD dementia (ADD), 86 amnestic MCI, and 33 healthy subjects enter the EEG study. All ADD subjects, 37 out of 86 MCI patients and a distinct group of 53 healthy controls further entered the fMRI study. MCI subjects were divided according to the CSF phosphorylated tau/β amyloid-42 ratio (MCIpos: ≥ 0.13, MCIneg: < 0.13). Using Exact low-resolution brain electromagnetic tomography (eLORETA), EEG lobar current densities were estimated at fixed frequencies and analyzed. To combine the two imaging techniques, networks mostly affected by AD pathology were identified using Independent Component Analysis applied to fMRI data of ADD subjects. Current density EEG analysis within ICA-based networks at selected frequency bands was performed. Afterwards, graph analysis was applied to EEG and fMRI data at ICA-based network level. Results ADD patients showed a widespread slowing of spectral density. At a lobar level, MCIpos subjects showed a widespread higher theta density than MCIneg and healthy subjects; a lower beta2 density than healthy subjects was also found in parietal and occipital lobes. Evaluating EEG sources within the ICA-based networks, alpha2 band distinguished MCIpos from MCIneg, ADD and healthy subjects with good accuracy. Graph analysis on EEG data showed an alteration of connectome configuration at theta frequency in ADD and MCIpos patients and a progressive disruption of connectivity at alpha2 frequency throughout the AD continuum. Conclusions Theta frequency is the earliest and most sensitive EEG marker of AD pathology. Furthermore, EEG/fMRI integration highlighted the role of alpha2 band as potential neurodegeneration biomarker.

Background Functional brain connectivity alterations may be detectable even before the occurrence of brain atrophy, indicating their potential as early markers of pathological processes. Objective We aimed to determine the whole-brain network topologic organization of the functional connectome in a large cohort of drug-naive Parkinson's disease (PD) patients using resting-state functional magnetic resonance imaging and to explore whether baseline connectivity changes may predict clinical progression. Methods One hundred and forty-seven drug-naive, cognitively unimpaired PD patients were enrolled in the study at baseline and compared to 38 age- and gender-matched controls. Non-hierarchical cluster analysis using motor and non-motor data was applied to stratify PD patients into two subtypes: 77 early/mild and 70 early/severe. Graph theory analysis and connectomics were used to assess global and local topological network properties and regional functional connectivity at baseline. Stepwise multivariate regression analysis investigated whether baseline functional imaging data were predictors of clinical progression over 2 years. Results At baseline, widespread functional connectivity abnormalities were detected in the basal ganglia, sensorimotor, frontal, and occipital networks in PD patients compared to controls. Decreased regional functional connectivity involving mostly striato-frontal, temporal, occipital, and limbic connections differentiated early/mild from early/severe PD patients. Connectivity changes were found to be independent predictors of cognitive progression at 2-year follow-up. Conclusions Our findings revealed that functional reorganization of the brain connectome occurs early in PD and underlies crucial involvement of striatal projections. Connectomic measures may be helpful to identify a specific PD patient subtype, characterized by severe motor and non-motor clinical burden as well as widespread functional connectivity abnormalities. © 2021 International Parkinson and Movement Disorder Society.

Background and ObjectivesTo assess cortical, subcortical, and cerebellar gray matter (GM) atrophy using MRI in patients with disorders of the frontotemporal lobar degeneration (FTLD) spectrum with known genetic mutations.MethodsSixty-six patients carrying FTLD-related mutations were enrolled, including 44 with pure motor neuron disease (MND) and 22 with frontotemporal dementia (FTD). Sixty-one patients with sporadic FTLD (sFTLD) matched for age, sex, and disease severity with genetic FTLD (gFTLD) were also included, as well as 52 healthy controls. A whole-brain voxel-based morphometry (VBM) analysis was performed. GM volumes of subcortical and cerebellar structures were obtained.ResultsCompared with controls, GM atrophy on VBM was greater and more diffuse in genetic FTD, followed by sporadic FTD and genetic MND cases, whereas patients with sporadic MND (sMND) showed focal motor cortical atrophy. Patients carrying C9orf72 and GRN mutations showed the most widespread cortical volume loss, in contrast with GM sparing in SOD1 and TARDBP. Globally, patients with gFTLD showed greater atrophy of parietal cortices and thalami compared with sFTLD. In volumetric analysis, patients with gFTLD showed volume loss compared with sFTLD in the caudate nuclei and thalami, in particular comparing C9-MND with sMND cases. In the cerebellum, patients with gFTLD showed greater atrophy of the right lobule VIIb than sFTLD. Thalamic volumes of patients with gFTLD with a C9orf72 mutation showed an inverse correlation with Frontal Behavioral Inventory scores.DiscussionMeasures of deep GM and cerebellar structural involvement may be useful markers of gFTLD, particularly C9orf72-related disorders, regardless of the clinical presentation within the FTLD spectrum.

ObjectiveTo investigate structural and functional neural organization in amyotrophic lateral sclerosis (ALS), primary lateral sclerosis (PLS), and progressive muscular atrophy (PMA).MethodsA total of 173 patients with sporadic ALS, 38 patients with PLS, 28 patients with PMA, and 79 healthy controls were recruited from 3 Italian centers. Participants underwent clinical, neuropsychological, and brain MRI evaluations. Using graph analysis and connectomics, global and lobar topologic network properties and regional structural and functional brain connectivity were assessed. The association between structural and functional network organization and clinical and cognitive data was investigated.ResultsCompared with healthy controls, patients with ALS and patients with PLS showed altered structural global network properties, as well as local topologic alterations and decreased structural connectivity in sensorimotor, basal ganglia, frontal, and parietal areas. Patients with PMA showed preserved global structure. Patient groups did not show significant alterations of functional network topologic properties relative to controls. Increased local functional connectivity was observed in patients with ALS in the precentral, middle, and superior frontal areas, and in patients with PLS in the sensorimotor, basal ganglia, and temporal networks. In patients with ALS and patients with PLS, structural connectivity alterations correlated with motor impairment, whereas functional connectivity disruption was closely related to executive dysfunction and behavioral disturbances.ConclusionsThis multicenter study showed widespread motor and extramotor network degeneration in ALS and PLS, suggesting that graph analysis and connectomics might represent a powerful approach to detect upper motor neuron degeneration, extramotor brain changes, and network reorganization associated with the disease. Network-based advanced MRI provides an objective in vivo assessment of motor neuron diseases, delivering potential prognostic markers.

Highlights • PCA patients present widespread loss of both intra- and inter-hemispheric connections. • Functional connectivity alterations were also observed in frontal connections. • They exceeded structural and metabolic brain damage. • Graph-analysis and connectomic have a high sensitivity in capturing the disease progression in PCA patients., This study investigated the functional brain connectome architecture in patients with Posterior Cortical Atrophy (PCA). Eighteen PCA patients and 29 age- and sex- matched healthy controls were consecutively recruited in a specialized referral center. Participants underwent neurologic examination, cerebrospinal fluid (CSF) examination for Alzheimer's disease (AD) biomarkers, cognitive assessment, and brain MRI. For a smaller subset of participants, FDG-PET examination was available. We assessed topological brain network properties and regional functional connectivity as well as intra- and inter-hemispheric connectivity, using graph analysis and connectomics. Supplementary analyses were performed to explore the association between the CSF AD profile and the connectome status, and taking into account hypometabolic, atrophic, and spared regions (nodes). PCA patients showed diffuse functional connectome alterations at both global and regional level, as well as a connectivity breakdown between the posterior brain nodes. They had a widespread loss of both intra- and inter-hemispheric connections, exceeding the structural damage, and including the frontal connections. In PCA, connectome alterations were identified in all the brain nodes irrespectively of their structural and metabolic classification and were associated with a connectivity breakdown between damaged and spared areas. Taken together, these findings suggest the potentially high sensitivity of graph-analysis and connectomic in capturing the progression and maybe early signs of neurodegeneration in PCA patients.

This paper describes the challenge of real-time tumor tissue identification dealt with by the HypErspectraL Imaging Cancer Detection (HELICoiD) European project. This project was funded by the Research Executive Agency, through the Future and Emerging Technologies (FET-Open) programme, under the 7th Framework Programme of the European Union. It involved four universities, three industrial partners and two hospitals. In this paper, we focused on the activity performed by the University of Las Palmas de Gran Canaria, in collaboration with the University of Pavia, concerning the parallel implementation of Support Vector Machine (SVM) classification for tumor tissue identification during surgery. Obtained results show that this classification is real-time compliant when performed using Graphic Processing Units (GPUs).