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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.

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.

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.

In the last few decades, brain functional connectivity (FC) has been extensively assessed using resting-state functional magnetic resonance imaging (RS-fMRI), which is able to identify temporally correlated brain regions known as RS functional networks. Fundamental insights into the pathophysiology of several neurodegenerative conditions have been provided by studies in this field. However, most of these studies are based on the assumption of temporal stationarity of RS functional networks, despite recent evidence suggests that the spatial patterns of RS networks may change periodically over the time of an fMRI scan acquisition. For this reason, dynamic functional connectivity (dFC) analysis has been recently implemented and proposed in order to consider the temporal fluctuations of FC. These approaches hold promise to provide fundamental information for the identification of pathophysiological and diagnostic markers in the vast field of neurodegenerative diseases. This review summarizes the main currently available approaches for dFC analysis and reports their recent applications for the assessment of the most common neurodegenerative conditions, including Alzheimer's disease, Parkinson's disease, dementia with Lewy bodies, and frontotemporal dementia. Critical state-of-the-art findings, limitations, and future perspectives regarding the analysis of dFC in these diseases are provided from both a clinical and a technical point of view.