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Abstract This study investigated longitudinal clinical, structural and functional brain alterations in Parkinson’s disease patients with freezing of gait (PD-FoG) and in those developing (PD-FoG-converters) and not developing FoG (PD-non-converters) over two years. Moreover, this study explored if any clinical and/or MRI metric predicts FoG development. Thirty PD-FoG, 11 PD-FoG-converters and 11 PD-non-converters were followed for two years. Thirty healthy controls were included at baseline. Participants underwent clinical and MRI visits. Cortical thickness, basal ganglia volumes and functional network graph metrics were evaluated at baseline and over time. In PD groups, correlations between baseline MRI and clinical worsening were tested. A ROC curve analysis investigated if baseline clinical and MRI measures, selected using a stepwise model procedure, could differentiate PD-FoG-converters from PD-non-converters. At baseline, PD-FoG patients had widespread cortical/subcortical atrophy, while PD-FoG-converters and non-converters showed atrophy in sensorimotor areas and basal ganglia relative to controls. Over time, PD-non-converters accumulated cortical thinning of left temporal pole and pallidum without significant clinical changes. PD-FoG-converters showed worsening of disease severity, executive functions, and mood together with an accumulation of occipital atrophy, similarly to PD-FoG. At baseline, PD-FoG-converters relative to controls and PD-FoG showed higher global and parietal clustering coefficient and global local efficiency. Over time, PD-FoG-converters showed reduced parietal clustering coefficient and sensorimotor local efficiency, PD-non-converters showed increased sensorimotor path length, while PD-FoG patients showed stable graph metrics. Stepwise prediction model including dyskinesia, postural instability and gait disorders scores and parietal clustering coefficient was the best predictor of FoG conversion. Combining clinical and MRI data, ROC curves provided the highest classification power to predict the conversion (AUC = 0.95, 95%CI: 0.86–1). Structural MRI is a useful tool to monitor PD progression, while functional MRI together with clinical features may be helpful to identify FoG conversion early.

Abstract Parkinson’s disease (PD) patients can be classified in tremor-dominant (TD) and postural-instability-and-gait-disorder (PIGD) motor subtypes. PIGD represents a more aggressive form of the disease that TD patients have a potentiality of converting into. This study investigated functional alterations within the cerebro-cerebellar system in PD-TD and PD-PIGD patients using stepwise functional connectivity (SFC) analysis and identified neuroimaging features that predict TD to PIGD conversion. Thirty-two PD-TD, 26 PD-PIGD patients and 60 healthy controls performed clinical/cognitive evaluations and resting-state functional MRI (fMRI). Four-year clinical follow-up data were available for 28 PD-TD patients, who were classified in 10 converters (cTD-PD) and 18 non-converters (ncTD-PD) to PIGD. The cerebellar seed-region was identified using a fMRI motor task. SFC analysis, characterizing regions that connect brain areas to the cerebellar seed at different levels of link-step distances, evaluated similar and divergent alterations in PD-TD and PD-PIGD. The discriminatory power of clinical data and/or SFC in distinguishing cPD-TD from ncPD-TD patients was assessed using ROC curve analysis. Compared to PD-TD, PD-PIGD patients showed decreased SFC in temporal lobe and occipital lobes and increased SFC in cerebellar cortex and ponto-medullary junction. Considering the subtype-conversion analysis, cPD-TD patients were characterized by increased SFC in temporal and occipital lobes and in cerebellum and ponto-medullary junction relative to ncPD-TD group. Combining clinical and SFC data, ROC curves provided the highest classification power to identify conversion to PIGD. These findings provide novel insights into the pathophysiology underlying different PD motor phenotypes and a potential tool for early characterization of PD-TD patients at risk of conversion to PIGD.

Abstract 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.

Abstract 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.

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.

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. Keywords: Posterior cortical atrophy (PCA), Human connectome, Functional connectivity, Graph analysis

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.