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Growing evidence suggests that a "prion-like" mechanism underlies the pathogenesis of many neurodegenerative disorders, including Parkinson's disease (PD). We extend and tailor previously developed quantitative and predictive network diffusion model (NDM) to PD, by specifically modeling the trans-neuronal spread of alpha-synuclein outward from the substantia nigra (SN). The model demonstrated the spatial and temporal patterns of PD from neuropathological and neuroimaging studies and was statistically validated using MRI deformation of 232 Parkinson's patients. After repeated seeding simulations, the SN was found to be the most likely seed region, supporting its unique lynchpin role in Parkinson's pathology spread. Other alternative spread models were also evaluated for comparison, specifically, random spread and distance-based spread; the latter tests for Braak's original caudorostral transmission theory. We showed that the distance-based spread model is not as well supported as the connectivity-based model. Intriguingly, the temporal sequencing of affected regions predicted by the model was in close agreement with Braak stages III-VI, providing what we consider a "computational Braak" staging system. Finally, we investigated whether the regional expression patterns of implicated genes contribute to regional atrophy. Despite robust evidence for genetic factors in PD pathogenesis, NDM outperformed regional genetic expression predictors, suggesting that network processes are far stronger mediators of regional vulnerability than innate or cell-autonomous factors. This is the first finding yet of the ramification of prion-like pathology propagation in Parkinson's, as gleaned from in vivo human imaging data. The NDM is potentially a promising robust and clinically useful tool for diagnosis, prognosis and staging of PD.

The article discusses the study which mapped the network structure and distribution of brain atrophy in Parkinson's Disease (PD) using magnetic resonance imaging (MRI) and clinical data of morphometry and independent component analysis for disease severity.

Purpose: Immunohistochemical (IHC) and histochemical (HC) staining techniques are widely used on human brains that are post-fixed in formalin and stored in brain banks worldwide for varying durations, from months to decades. Understanding the effects of prolonged post-fixation, postmortem interval (PMI), and age on these staining procedures is important for accurately interpreting their outcomes, thereby improving the diagnosis and research of brain disorders afflicting millions of people worldwide., Methods: In this study, we conducted both IHC and HC staining on the prefrontal cortex of postmortem human brains post-fixed for 1, 5, 10, 15, and 20 years. For IHC staining, we used two antibodies for each marker: the neuron marker neuronal nuclear antigen (NeuN), the astrocyte marker glial fibrillary acidic protein (GFAP), and the microglia marker ionized calcium-binding adaptor molecule 1 (Iba1). For HC staining, we conducted hematoxylin and eosin Y (H&E), cresyl violet (CV), and Luxol fast blue (LFB) stains to examine neuropils, neurons, and myelin, respectively., Results: We observed that the intensity of NeuN, Iba1, CV, or LFB staining was negatively correlated with post-fixation durations. Conversely, we detected a positive correlation between the intensity of GFAP and H&E staining and post-fixation durations. Moreover, there was no correlation between the intensity of NeuN, GFAP, Iba1, H&E, CV, and LFB staining and PMI. Additionally, no correlation was found between these staining intensities and age, except for the intensity of GFAP immunostained by one antiserum, which was negatively correlated with age., Conclusion: Taken together, these findings suggest that prolonged post-fixation has both positive and negative effects, while age and PMI exert limited influence on these IHC and HC parameters. Therefore, it is essential to consider these differential changes when interpreting results derived from tissues with extended post-fixation durations. Furthermore, if feasible, we recommend conducting IHC and HC staining on human brains with the same post-fixation time spans and using the most optimal antibodies to mitigate the impact on subsequent analyses., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ma, Frigon, Maranzano, Zeighami and Dadar.)

Obesity and its metabolic complications are associated with lower grey matter and white matter densities, whereas weight loss after bariatric surgery leads to an increase in both measures. These increases in grey and white matter density are significantly associated with post-operative weight loss and improvement of the metabolic/inflammatory profiles. While our recent studies demonstrated widespread increases in white matter density 4 and 12 months after bariatric surgery, it is not clear if these changes persist over time. The underlying mechanisms also remain unknown. In this regard, numerous studies demonstrate that the enlargement or hypertrophy of mature adipocytes, particularly in the visceral fat compartment, is an important marker of adipose tissue dysfunction and obesity-related cardiometabolic abnormalities. We aimed (i) to assess whether the increases in grey and white matter densities previously observed at 12 months are maintained 24 months after bariatric surgery; (ii) to examine the association between these structural brain changes and adiposity and metabolic markers 24 months after bariatric surgery; and (iii) to examine the association between abdominal adipocyte diameter at the time of surgery and post-surgery grey and white matter densities changes. Thirty-three participants undergoing bariatric surgery were recruited. Grey and white matter densities were assessed from T1-weighted magnetic resonance imaging scans acquired prior to and 4, 12 and 24 months post-surgery using voxel-based morphometry. Omental and subcutaneous adipose tissue samples were collected during the surgical procedure. Omental and subcutaneous adipocyte diameters were measured by microscopy of fixed adipose tissue samples. Linear mixed-effects models were performed controlling for age, sex, surgery type, initial body mass index, and initial diabetic status. The average weight loss at 24 months was 33.6 ± 7.6%. A widespread increase in white matter density was observed 24 months post-surgery mainly in the cerebellum, brainstem and corpus callosum ( P < 0.05, false discovery rate) as well as some regions in grey matter density. Greater omental adipocyte diameter at the time of surgery was associated with greater changes in total white matter density at 24 months ( P = 0.008). A positive trend was observed between subcutaneous adipocyte diameter at the time of surgery and changes in total white matter density at 24 months ( P = 0.05). Our results show prolonged increases in grey and white matter densities up to 24 months post-bariatric surgery. Greater preoperative omental adipocyte diameter is associated with greater increases in white matter density at 24 months, suggesting that individuals with excess visceral adiposity might benefit the most from surgery., Competing Interests: A.T. and L.B. receive research funding from Johnson & Johnson, Medtronic and GI Windows for studies on bariatric surgery. A.T. and L.B. acted as consultants for Bausch Health and Novo Nordisk. A.T. is a consultant for BioTwin. The other authors had no conflict of interest to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

For over a century, brain research narrative has mainly centered on neuron cells. Accordingly, most neurodegenerative studies focus on neuronal dysfunction and their selective vulnerability, while we lack comprehensive analyses of other major cell types' contribution. By unifying spatial gene expression, structural MRI, and cell deconvolution, here we describe how the human brain distribution of canonical cell types extensively predicts tissue damage in 13 neurodegenerative conditions, including early- and late-onset Alzheimer's disease, Parkinson's disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, mutations in presenilin-1, and 3 clinical variants of frontotemporal lobar degeneration (behavioral variant, semantic and non-fluent primary progressive aphasia) along with associated three-repeat and four-repeat tauopathies and TDP43 proteinopathies types A and C. We reconstructed comprehensive whole-brain reference maps of cellular abundance for six major cell types and identified characteristic axes of spatial overlapping with atrophy. Our results support the strong mediating role of non-neuronal cells, primarily microglia and astrocytes, in spatial vulnerability to tissue loss in neurodegeneration, with distinct and shared across-disorder pathomechanisms. These observations provide critical insights into the multicellular pathophysiology underlying spatiotemporal advance in neurodegeneration. Notably, they also emphasize the need to exceed the current neuro-centric view of brain diseases, supporting the imperative for cell-specific therapeutic targets in neurodegeneration., Competing Interests: VP, QA, DB, MD, YZ, YI No competing interests declared, (© 2023, Pak et al.)

Parkinson's disease involves multiple neurotransmitter systems beyond the classical dopaminergic circuit, but their influence on structural and functional alterations is not well understood. Here, we use patient-specific causal brain modeling to identify latent neurotransmitter receptor-mediated mechanisms contributing to Parkinson's disease progression. Combining the spatial distribution of 15 receptors from post-mortem autoradiography with 6 neuroimaging-derived pathological factors, we detect a diverse set of receptors influencing gray matter atrophy, functional activity dysregulation, microstructural degeneration, and dendrite and dopaminergic transporter loss. Inter-individual variability in receptor mechanisms correlates with symptom severity along two distinct axes, representing motor and psychomotor symptoms with large GABAergic and glutamatergic contributions, and cholinergically-dominant visuospatial, psychiatric and memory dysfunction. Our work demonstrates that receptor architecture helps explain multi-factorial brain re-organization, and suggests that distinct, co-existing receptor-mediated processes underlie Parkinson's disease., (© 2023. The Author(s).)

Background and Objectives: White matter hyperintensities (WMH) are pathologic brain changes that are associated with increased age and cognitive decline. However, the association of WMH burden with amyloid positivity and conversion to dementia in people with mild cognitive impairment (MCI) is unclear. The aim of this study was to expand on this research by examining whether change in WMH burden over time differs in amyloid-negative (Aβ-) and amyloid-positive (Aβ+) people with MCI who either remain stable or convert to dementia. To examine this question, we compared regional WMH burden in 4 groups: Aβ+ progressor, Aβ- progressor, Aβ+ stable, and Aβ- stable., Methods: Participants with MCI from the Alzheimer Disease Neuroimaging Initiative were included if they had APOE ɛ4 status and if amyloid measures were available to determine amyloid status (i.e., Aβ+, or Aβ-). Participants with a baseline diagnosis of MCI and who had APOE ɛ4 information and amyloid measures were included. An average of 5.7 follow-up time points per participant were included, with a total of 5,054 follow-up time points with a maximum follow-up duration of 13 years. Differences in total and regional WMH burden were examined using linear mixed-effects models., Results: A total of 820 participants (55-90 years of age) were included in the study (Aβ+ progressor, n = 239; Aβ- progressor, n = 22; Aβ+ stable, n = 343; Aβ- stable, n = 216). People who were Aβ- stable exhibited reduced baseline WMH compared with Aβ+ progressors and people who were Aβ+ stable at all regions of interest (β belongs to 0.20-0.33, CI belongs to 0.03-0.49, p < 0.02), except deep WMH. When examining longitudinal results, compared with people who were Aβ- stable, all groups had steeper accumulation in WMH burden with Aβ+ progressors (β belongs to -0.03 to 0.06, CI belongs to -0.05 to 0.09, p < 0.01) having the largest increase (i.e., largest increase in WMH accumulation over time)., Discussion: These results indicate that WMH accumulation contributes to conversion to dementia in older adults with MCI who are Aβ+ and Aβ-., (© 2023 American Academy of Neurology.)

Genes associated with risk for brain disease exhibit characteristic expression patterns that reflect both anatomical and cell type relationships. Brain-wide transcriptomic patterns of disease risk genes provide a molecular-based signature, based on differential co-expression, that is often unique to that disease. Brain diseases can be compared and aggregated based on the similarity of their signatures which often associates diseases from diverse phenotypic classes. Analysis of 40 common human brain diseases identifies 5 major transcriptional patterns, representing tumor-related, neurodegenerative, psychiatric and substance abuse, and 2 mixed groups of diseases affecting basal ganglia and hypothalamus. Further, for diseases with enriched expression in cortex, single-nucleus data in the middle temporal gyrus (MTG) exhibits a cell type expression gradient separating neurodegenerative, psychiatric, and substance abuse diseases, with unique excitatory cell type expression differentiating psychiatric diseases. Through mapping of homologous cell types between mouse and human, most disease risk genes are found to act in common cell types, while having species-specific expression in those types and preserving similar phenotypic classification within species. These results describe structural and cellular transcriptomic relationships of disease risk genes in the adult brain and provide a molecular-based strategy for classifying and comparing diseases, potentially identifying novel disease relationships., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Zeighami et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

White matter hyperintensities (WMHs) are pathological changes that occur with increased age and are associated with cognitive decline. Most WMH research has not examined regional differences and focuses on a whole-brain approach. This study examined regional WMHs between normal controls (NCs), people with mild cognitive impairment (MCI), and Alzheimer's disease (AD). We also examined whether WMHs were associated with cognitive decline. Participants from the Alzheimer's Disease Neuroimaging Initiative were included if they had at least one WMH measurement and cognitive scores examining global cognition, executive functioning, and memory. Only amyloid-positive MCI and AD participants were included. A total of 1573 participants with 7381 timepoints over a maximum period of 13 years were included. Linear mixed-effects models examined group differences in WMH burden and associations between WMH burden and cognition. People with MCI and AD had increased total and regional WMHs compared to NCs. An association between WMHs and cognition was observed for global cognition, executive functioning, and memory in NCs in all regions. A steeper decline (stronger association between WMH and cognition) was observed in MCI compared to NCs for all cognitive domains in all regions. A steeper decline was observed in AD compared to NCs for global cognition in only the temporal region. A strong association is observed between all cognitive domains of interest and WMH burden in healthy aging and MCI, while those with AD only had a few associations between WMH and global cognition. These findings suggest that the WMH burden is associated with changes in cognition in healthy aging and early cognitive decline., (© 2022. The Author(s), under exclusive licence to American Aging Association.)

Essential tremor (ET) is the most prevalent movement disorder with poorly understood etiology. Some neuroimaging studies report cerebellar involvement whereas others do not. This discrepancy may stem from underpowered studies, differences in statistical modeling or variation in magnetic resonance imaging (MRI) acquisition and processing. To resolve this, we investigated the cerebellar structural differences using a local advanced ET dataset augmented by matched controls from PPMI and ADNI. We tested the hypothesis of cerebellar involvement using three neuroimaging biomarkers: VBM, gray/white matter volumetry and lobular volumetry. Furthermore, we assessed the impacts of statistical models and segmentation pipelines on results. Results indicate that the detected cerebellar structural changes vary with methodology. Significant reduction of right cerebellar gray matter and increase of the left cerebellar white matter were the only two biomarkers consistently identified by multiple methods. Results also show substantial volumetric overestimation from SUIT-based segmentation-partially explaining previous literature discrepancies. This study suggests that current estimation of cerebellar involvement in ET may be overemphasized in MRI studies and highlights the importance of methods sensitivity analysis on results interpretation. ET datasets with large sample size and replication studies are required to improve our understanding of regional specificity of cerebellum involvement in ET. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 21 March 2022. The protocol, as accepted by the journal, can be found at: https://doi.org/10.6084/m9.figshare.19697776 ., (© 2023. The Author(s).)

Background: Excess weight in adulthood leads to health complications such as diabetes, hypertension, or dyslipidemia. Recently, excess weight has also been related to brain atrophy and cognitive decline. Reports show that obesity is linked with Alzheimer's disease (AD)-related changes, such as cerebrovascular damage or amyloid-β accumulation. However, to date no research has conducted a direct comparison between brain atrophy patterns in AD and obesity., Objective: Here, we compared patterns of brain atrophy and amyloid-β/tau protein accumulation in obesity and AD using a sample of over 1,300 individuals from four groups: AD patients, healthy controls, obese otherwise healthy individuals, and lean individuals., Methods: We age- and sex-matched all groups to the AD-patients group and created cortical thickness maps of AD and obesity. This was done by comparing AD patients with healthy controls, and obese individuals with lean individuals. We then compared the AD and obesity maps using correlation analyses and permutation-based tests that account for spatial autocorrelation. Similarly, we compared obesity brain maps with amyloid-β and tau protein maps from other studies., Results: Obesity maps were highly correlated with AD maps but were not correlated with amyloid-β/tau protein maps. This effect was not accounted for by the presence of obesity in the AD group., Conclusion: Our research confirms that obesity-related grey matter atrophy resembles that of AD. Excess weight management could lead to improved health outcomes, slow down cognitive decline in aging, and lower the risk for AD.

Individuals living with obesity tend to have increased brain age, reflecting poorer brain health likely due to grey and white matter atrophy related to obesity. However, it is unclear if older brain age associated with obesity can be reversed following weight loss and cardiometabolic health improvement. The aim of this study was to assess the impact of weight loss and cardiometabolic improvement following bariatric surgery on brain health, as measured by change in brain age estimated based on voxel-based morphometry (VBM) measurements. We used three distinct datasets to perform this study: 1) CamCAN dataset to train the brain age prediction model, 2) Human Connectome Project (HCP) dataset to investigate whether individuals with obesity have greater brain age than individuals with normal weight, and 3) pre-surgery, as well as 4, 12, and 24 month post-surgery data from participants (n = 87, age: 44.0 ± 9.2 years, BMI: 43.9 ± 4.2 kg/m 2 ) who underwent a bariatric surgery to investigate whether weight loss and cardiometabolic improvement as a result of bariatric surgery lowers the brain age. As expected, our results from the HCP dataset showed a higher brain age for individuals with obesity compared to individuals with normal weight (T-value = 7.08, p-value < 0.0001). We also found significant improvement in brain health, indicated by a decrease of 2.9 and 5.6 years in adjusted delta age at 12 and 24 months following bariatric surgery compared to baseline (p-value < 0.0005 for both). While the overall effect seemed to be driven by a global change across all brain regions and not from a specific region, our exploratory analysis showed lower delta age in certain brain regions (mainly in somatomotor, visual, and ventral attention networks) at 24 months. This reduced age was also associated with post-surgery improvements in BMI, systolic/diastolic blood pressure, and HOMA-IR (T-value BMI =4.29, T-value SBP =4.67, T-value DBP =4.12, T-value HOMA-IR =3.16, all p-values < 0.05). In conclusion, these results suggest that obesity-related brain health abnormalities (as measured by delta age) might be reversed by bariatric surgery-induced weight loss and widespread improvements in cardiometabolic alterations., Competing Interests: Declaration of Competing Interest A. T. and L. B. are recipients of research grant support from Johnson & Johnson Medical Companies and Medtronic for studies on bariatric surgery and the Research Chair in Bariatric and Metabolic Surgery at IUCPQ and Laval University. AT has received consulting fees from Bausch Health, Novo Nordisk and acts as a consultant for Biotwin. No author declared a conflict of interest relevant to the content of the manuscript., (Copyright © 2022. Published by Elsevier Inc.)

Due to the marked interpersonal neuropathologic and clinical heterogeneity of Parkinson's disease (PD), current interventions are not personalized and fail to benefit all patients. Furthermore, we continue to lack well-established methods and clinical tests to tailor interventions at the individual level in PD. Here, we identify the genetic determinants of individual-tailored treatment needs derived from longitudinal multimodal neuroimaging data in 294 PD patients (PPMI data). Advanced multivariate statistical analysis revealed that both genomic and blood transcriptomic data significantly explain (P < 0.01, FWE-corrected) the interindividual variability in therapeutic needs associated with dopaminergic, functional, and structural brain reorganization. We confirmed a high overlap between the identified highly predictive molecular pathways and determinants of levodopa clinical responsiveness, including well-known (Wnt signaling, angiogenesis, dopaminergic activity) and recently discovered (immune markers, gonadotropin-releasing hormone receptor) pathways/components. In addition, the observed strong correspondence between the identified genomic and baseline-transcriptomic determinants of treatment needs/response supports the genome's active role at the time of patient evaluation (i.e., beyond individual genetic predispositions at birth). This study paves the way for effectively combining genomic, transcriptomic and neuroimaging data for implementing successful individually tailored interventions in PD and extending our pathogenetic understanding of this multifactorial and heterogeneous disorder., (© 2022. The Author(s).)

Background: Impulsivity increases the risk for obesity and weight gain. However, the precise role of impulsivity in the aetiology of overeating behavior and obesity is currently unknown. Here we examined the relationships between personality-related measures of impulsivity, Uncontrolled Eating, body mass index (BMI), and longitudinal weight changes. In addition, we analyzed the associations between general impulsivity domains and cortical thickness to elucidate brain vulnerability factors related to weight gain., Methods: Students (N = 2318) in their first year of university-a risky period for weight gain-completed questionnaire measures of impulsivity and eating behavior at the beginning of the school year. We also collected their weight at the end of the term (N = 1177). Impulsivity was divided into three factors: stress reactivity, reward sensitivity and lack of self-control. Using structural equation models, we tested a hierarchical relationship, in which impulsivity traits were associated with Uncontrolled Eating, which in turn predicted BMI and weight change. Seventy-one participants underwent T1-weighted MRI to investigate the correlation between impulsivity and cortical thickness., Results: Impulsivity traits showed positive correlations with Uncontrolled Eating. Higher scores in Uncontrolled Eating were in turn associated with higher BMI. None of the impulsivity-related measurements nor Uncontrolled Eating were correlated with longitudinal weight gain. Higher stress sensitivity was associated with increased cortical thickness in the superior temporal gyrus. Lack of self-control was positively associated with increased thickness in the superior medial frontal gyrus. Finally, higher reward sensitivity was associated with lower thickness in the inferior frontal gyrus., Conclusion: The present study provides a comprehensive characterization of the relationships between different facets of impulsivity and obesity. We show that differences in impulsivity domains might be associated with BMI via Uncontrolled Eating. Our results might inform future clinical strategies aimed at fostering self-control abilities to prevent and/or treat unhealthy weight gain., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: Metabolic disorders associated with obesity could lead to alterations in brain structure and function. Whether these changes can be reversed after weight loss is unclear. Bariatric surgery provides a unique opportunity to address these questions because it induces marked weight loss and metabolic improvements which in turn may impact the brain in a longitudinal fashion. Previous studies found widespread changes in grey matter (GM) and white matter (WM) after bariatric surgery. However, findings regarding changes in spontaneous neural activity following surgery, as assessed with the fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity of neural activity (ReHo), are scarce and heterogenous. In this study, we used a longitudinal design to examine the changes in spontaneous neural activity after bariatric surgery (comparing pre- to post-surgery), and to determine whether these changes are related to cardiometabolic variables., Methods: The study included 57 participants with severe obesity (mean BMI=43.1 ± 4.3 kg/m 2 ) who underwent sleeve gastrectomy (SG), biliopancreatic diversion with duodenal switch (BPD), or Roux-en-Y gastric bypass (RYGB), scanned prior to bariatric surgery and at follow-up visits of 4 months (N = 36), 12 months (N = 29), and 24 months (N = 14) after surgery. We examined fALFF and ReHo measures across 1022 cortical and subcortical regions (based on combined Schaeffer-Xiao parcellations) using a linear mixed effect model. Voxel-based morphometry (VBM) based on T1-weighted images was also used to measure GM density in the same regions. We also used an independent sample from the Human Connectome Project (HCP) to assess regional differences between individuals who had normal-weight (N = 46) or severe obesity (N = 46)., Results: We found a global increase in the fALFF signal with greater increase within dorsolateral prefrontal cortex, precuneus, inferior temporal gyrus, and visual cortex. This effect was more significant 4 months after surgery. The increase within dorsolateral prefrontal cortex, temporal gyrus, and visual cortex was more limited after 12 months and only present in the visual cortex after 24 months. These increases in neural activity measured by fALFF were also significantly associated with the increase in GM density following surgery. Furthermore, the increase in neural activity was significantly related to post-surgery weight loss and improvement in cardiometabolic variables, such as blood pressure. In the independent HCP sample, normal-weight participants had higher global and regional fALFF signals, mainly in dorsolateral/medial frontal cortex, precuneus and middle/inferior temporal gyrus compared to the obese participants. These BMI-related differences in fALFF were associated with the increase in fALFF 4 months post-surgery especially in regions involved in control, default mode and dorsal attention networks., Conclusions: Bariatric surgery-induced weight loss and improvement in metabolic factors are associated with widespread global and regional increases in neural activity, as measured by fALFF signal. These findings alongside the higher fALFF signal in normal-weight participants compared to participants with severe obesity in an independent dataset suggest an early recovery in the neural activity signal level after the surgery., (Copyright © 2021. Published by Elsevier Inc.)

Some Diffusion Tensor Imaging studies have shown a loss of white matter (WM) integrity linked to impaired cognitive function in obese individuals. However, inconsistent WM integrity changes have been reported. We aimed to identify which WM tracts show consistent changes with obesity. We conducted a systematic search to find studies examining the association between obesity-related measures and Fractional Anisotropy (FA) or Mean Diffusivity. We performed a meta-analysis with FA datasets using Anisotropic Effect Size-Signed Differential Mapping software. The meta-analysis showed that increased obesity measurements were related to reduced FA in the genu of the corpus callosum. We validated our findings using an independent sample from the Human Connectome Project dataset, which supports lower FA in this region in individuals with obesity compared to those with normal weight (p = 0.028). Our findings provide evidence that obesity is associated with reduced WM integrity in the genu of the corpus callosum, a tract linking frontal areas involved in executive function. Future studies are needed on the mechanisms linking obesity with loss of WM integrity., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Association and prediction studies of the brain target the biological consequences of aging and their impact on brain function. Such studies are conducted using different smoothing levels and parcellations at the preprocessing stage, on which their results are dependent. However, the impact of these parameters on the relationship between association values and prediction accuracy is not established. In this study, we used cortical thickness and its relationship with age to investigate how different smoothing and parcellation levels affect the detection of age-related brain correlates as well as brain age prediction accuracy. Our main measures were resel numbers-resolution elements-and age-related variance explained. Using these common measures enabled us to directly compare parcellation and smoothing effects in both association and prediction studies. In our sample of N = 608 participants with age range 18-88, we evaluated age-related cortical thickness changes as well as brain age prediction. We found a negative relationship between prediction performance and correlation values for both parameters. Our results also quantify the relationship between delta age estimates obtained based on different processing parameters. Furthermore, with the direct comparison of the two approaches, we highlight the importance of correct choice of smoothing and parcellation parameters in each task, and how they can affect the results of the analysis in opposite directions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Zeighami and Evans.)

Negative symptoms such as anhedonia and apathy are among the most debilitating manifestations of schizophrenia (SZ). Imaging studies have linked these symptoms to morphometric abnormalities in 2 brain regions implicated in reward and motivation: the orbitofrontal cortex (OFC) and striatum. Higher negative symptoms are generally associated with reduced OFC thickness, while higher apathy specifically maps to reduced striatal volume. However, it remains unclear whether these tissue losses are a consequence of chronic illness and its treatment or an underlying phenotypic trait. Here, we use multicentre magnetic resonance imaging data to investigate orbitofrontal-striatal abnormalities across the SZ spectrum from healthy populations with high schizotypy to unmedicated and medicated first-episode psychosis (FEP), and patients with chronic SZ. Putamen, caudate, accumbens volume, and OFC thickness were estimated from T1-weighted images acquired in all 3 diagnostic groups and controls from 4 sites (n = 337). Results were first established in 1 discovery dataset and replicated in 3 independent samples. There was a negative correlation between apathy and putamen/accumbens volume only in healthy individuals with schizotypy; however, medicated patients exhibited larger putamen volume, which appears to be a consequence of antipsychotic medications. The negative association between reduced OFC thickness and total negative symptoms also appeared to vary along the SZ spectrum, being significant only in FEP patients. In schizotypy, there was increased OFC thickness relative to controls. Our findings suggest that negative symptoms are associated with a temporal continuum of orbitofrontal-striatal abnormalities that may predate the occurrence of SZ. Thicker OFC in schizotypy may represent either compensatory or pathological mechanisms prior to the disease onset., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Competing Interests: The authors declare that there are no conflicts of interest relevant to this work.

Parkinson's disease varies in severity and age of onset. One source of this variability is sex. Males are twice as likely as females to develop Parkinson's disease, and tend to have more severe symptoms and greater speed of progression. However, to date, there is little information in large cohorts on sex differences in the patterns of neurodegeneration. Here we used MRI and clinical information from the Parkinson Progression Markers Initiative to measure structural brain differences between sexes in Parkinson's disease after regressing out the expected effect of age and sex. We derived atrophy maps from deformation-based morphometry of T1-weighted MRI and connectivity from diffusion-weighted MRI in de novo Parkinson's disease patients (149 males: 83 females) with comparable clinical severity, and healthy control participants (78 males: 39 females). Overall, even though the two patient groups were matched for disease duration and severity, males demonstrated generally greater brain atrophy and disrupted connectivity. Males with Parkinson's disease had significantly greater tissue loss than females in 11 cortical regions including bilateral frontal and left insular lobe, right postcentral gyrus, left inferior temporal and cingulate gyrus and left thalamus, while females had greater atrophy in six cortical regions, including regions in the left frontal lobe, right parietal lobe, left insular gyrus and right occipital cortex. Local efficiency of white matter connectivity showed greater disruption in males in multiple regions such as basal ganglia, hippocampus, amygdala and thalamus. These findings support the idea that development of Parkinson's disease may involve different pathological mechanisms and yield distinct prognosis in males and females, which may have implications for research into neuroprotection, and stratification for clinical trials., (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

In recent years, replicability of neuroscientific findings, specifically those concerning correlates of morphological properties of gray matter (GM), have been subject of major scrutiny. Use of different processing pipelines and differences in their estimates of the macroscale GM may play an important role in this context. To address this issue, here, we investigated the cortical thickness estimates of three widely used pipelines. Based on analyses in two independent large-scale cohorts, we report high levels of within-pipeline reliability of the absolute cortical thickness-estimates and comparable spatial patterns of cortical thickness-estimates across all pipelines. Within each individual, absolute regional thickness differed between pipelines, indicating that in-vivo thickness measurements are only a proxy of actual thickness of the cortex, which shall only be compared within the same software package and thickness estimation technique. However, at group level, cortical thickness-estimates correlated strongly between pipelines, in most brain regions. The smallest between-pipeline correlations were observed in para-limbic areas and insula. These regions also demonstrated the highest interindividual variability and the lowest reliability of cortical thickness-estimates within each pipeline, suggesting that structural variations within these regions should be interpreted with caution., (© The Author(s) 2020. Published by Oxford University Press.)

Background: Patients with Parkinson's disease (PD) present with a broad spectrum of nonmotor features including autonomic disorders. More severe autonomic dysfunction in PD is associated with increased cognitive deficits. The presence of cerebral small-vessel disease, measured by T2-weighted magnetic resonance imaging white matter hyperintensity (WMH) burden, is also observed in patients with PD with faster cognitive decline., Objective: To investigate whether baseline orthostatic hypotension and autonomic dysfunction in early-stage PD affect later cognitive decline via mediation through cerebral small-vessel disease., Methods: De novo PD patients (N = 365) and age-matched controls (N = 174) with baseline T2-weighted/ fluid-attenuated inversion recovery scans were selected from the Parkinson's Progression Markers Initiative. WMHs were automatically segmented. Mediation analysis was used to assess whether WMH load mediates the effect of orthostatic hypotension and autonomic dysfunction (measured by Scales for Outcomes in Parkinson's Disease-Autonomic) on future cognitive decline (measured by Montreal Cognitive Assessment) in an average of 4 years of follow-up., Results: Mediation analysis supported the existence of a full mediation of WMHs on the effect of diastolic orthostatic hypotension in patients with PD and future cognitive decline (average causal mediation effect: ab = -0.032, 95% confidence interval = -0.064 to -0.01, P  = 0.01). There was also a partial mediation for overall autonomic dysfunction (ab = -0.027, 95% confidence interval = -0.054 to 0.00, P  = 0.02)., Conclusions: WMHs fully mediate the effect of diastolic orthostatic hypotension and partially mediate the effect of autonomic dysregulation on future cognitive decline in patients with PD. Our findings support the hypothesis that autonomic dysfunction in early clinical stages predisposes the brain to WMHs through dysregulation of the blood flow in the small vessels. This in turn increases the risk of future cognitive impairment in early PD., (© 2020 International Parkinson and Movement Disorder Society.)

Autism spectrum disorder is a highly prevalent and highly heritable neurodevelopmental condition, but studies have mostly taken traditional categorical diagnosis approach (yes/no for autism spectrum disorder). In contrast, an emerging notion suggests a continuum model of autism spectrum disorder with a normal distribution of autistic tendencies in the general population, where a full diagnosis is at the severe tail of the distribution. We set out to investigate such a viewpoint by investigating the interaction of polygenic risk scores for autism spectrum disorder and Age 2 on neuroimaging measures (cortical thickness and white matter connectivity) in a general population ( n  =   391, with age ranging from 3 to 21 years from the Pediatric Imaging, Neurocognition and Genetics study). We observed that children with higher polygenic risk for autism spectrum disorder exhibited greater cortical thickness for a large age span starting from 3 years up to ∼14 years in several cortical regions localized in bilateral precentral gyri and the left hemispheric postcentral gyrus and precuneus. In an independent case-control dataset from the Autism Brain Imaging Data Exchange ( n  =   560), we observed a similar pattern: children with autism spectrum disorder exhibited greater cortical thickness starting from 6 years onwards till ∼14 years in wide-spread cortical regions including (the ones identified using the general population). We also observed statistically significant regional overlap between the two maps, suggesting that some of the cortical abnormalities associated with autism spectrum disorder overlapped with brain changes associated with genetic vulnerability for autism spectrum disorder in healthy individuals. Lastly, we observed that white matter connectivity between the frontal and parietal regions showed significant association with polygenic risk for autism spectrum disorder, indicating that not only the brain structure, but the white matter connectivity might also show a predisposition for the risk of autism spectrum disorder. Our findings showed that the fronto-parietal thickness and connectivity are dimensionally related to genetic risk for autism spectrum disorder in general population and are also part of the cortical abnormalities associated with autism spectrum disorder. This highlights the necessity of considering continuum models in studying the aetiology of autism spectrum disorder using polygenic risk scores and multimodal neuroimaging., (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain.)

There is enormous clinical value in inferring the brain regions initially atrophied in Parkinson disease for individual patients and understanding its relationship with clinical and genetic risk factors. The aim of this study is to leverage a new seed-inference algorithm demonstrated for Alzheimer's disease to the Parkinsonian context and to cluster patients in meaningful subgroups based on these incipient atrophy patterns. Instead of testing brain regions separately as the likely initiation site for each patient, we solve an L1-penalized optimization problem that can return a more predictive heterogeneous, multi-locus seed patterns. A cluster analysis of the individual seed patterns reveals two distinct subgroups (S1 versus S2). The S1 subgroup is characterized by the involvement of the brainstem and ventral nuclei, and S2 by cortex and striatum. Post hoc analysis in features not included in the clustering shows significant differences between subgroups regarding age of onset and local transcriptional patterns of Parkinson-related genes. Top genes associated with regional microglial abundance are strongly associated with subgroup S1 but not with S2. Our results suggest two distinct aetiological mechanisms operative in Parkinson disease. The interplay between immune-related genes, lysosomal genes, microglial abundance and atrophy initiation sites may explain why the age of onset for patients in S1 is on average 4.5 years later than for those in S2. We highlight and compare the most prominently affected brain regions for both subgroups. Altogether, our findings may improve current screening strategies for early Parkinson onsetters., (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain.)

Background: MRI studies show that obese adults have reduced grey matter (GM) and white matter (WM) tissue density as well as altered WM integrity. Bariatric surgery can lead to substantial weight loss and improvements in metabolic parameters, but it remains to be examined if it induces structural brain changes. The aim of this study was to characterize GM and WM density changes measured with MRI in a longitudinal setting following sleeve gastrectomy, and to determine whether any changes are related to inflammation and cardiometabolic blood markers., Methods: 29 participants with obesity (age: 45.9 ​± ​7.8 years) scheduled to undergo sleeve gastrectomy were recruited. High-resolution T1-weighted anatomical images were acquired 1 month prior to as well as 4 and 12 months after surgery. GM and WM densities were quantified using voxel-based morphometry (VBM). Circulating lipid profile, glucose, insulin and inflammatory markers (interleukin-6, C-reactive protein and lipopolysaccharide-binding protein) were measured at each time point. A linear mixed effect model was used to compare brain changes before and after SG, controlling for age, sex, initial BMI and diabetic status. To assess the associations between changes in adiposity, metabolism and inflammation and changes in GM or WM density, the mean GM and WM densities were extracted across all the participants using atlas-derived regions of interest, and linear mixed-effect models were used., Results: As expected, weight, BMI, waist circumference and neck circumference significantly decreased after SG compared with baseline (p ​< ​0.001 for all). A widespread increase in WM density was observed after surgery, particularly in the cerebellum, brain stem, cerebellar peduncle, cingulum, corpus callosum and corona radiata (p ​< ​0.05, after FDR correction). Significant increases in GM density were observed 4 months after SG compared to baseline in several brain regions such as the bilateral occipital cortex, temporal cortex, postcentral gyrus, cerebellum, hippocampus and insula as well as right fusiform gyrus, right parahippocampal gyrus, right lingual gyrus and right amygdala. These GM and WM increases were more pronounced and widespread after 12 months and were significantly associated with post-operative weight loss and the improvement of metabolic alterations. A linear mixed-effect model also showed associations between post-operative reductions in lipopolysaccharide-binding protein, a marker of inflammation, and increased WM density. To confirm our results, we tested whether the peak of each significant region showed BMI-related differences in an independent dataset (Human Connectome Project). We matched a group of individuals who were severely obese with a group of individuals who were lean for age, sex and ethnicity. Severe obesity was associated with reduced WM density in the brain stem and cerebellar peduncle as well as reduced GM density in cerebellum, regions that significantly changed after surgery (p ​< ​0.01 for all clusters)., Conclusions: Bariatric surgery-induced weight loss and improvement in metabolic alterations is associated with widespread increases in WM and GM densities. These post-operative changes overlapped with baseline brain differences between participants who were severely obese and those who were normal-weight in a separate dataset, which may suggest a recovery of WM and GM alterations after bariatric surgery., Competing Interests: Declaration of competing interest A. T. and L.B. are recipients of research grant support from Johnson & Johnson Medical Companies and Medtronic for studies on bariatric surgery and the Research Chair in Bariatric and Metabolic Surgery at IUCPQ and Laval University, respectively. No author declared a conflict to interest relevant to the content of the manuscript., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Alterations in dopamine signaling play a key role in reinforcement learning and nicotine addiction, but the relationship between these two processes has not been well characterized. We investigated this relationship in young adult smokers using a combination of behavioral and computational measures of reinforcement learning., Methods: We asked moderately dependent smokers to engage in a reinforcement learning task three times: smoking as usual, smoking abstinence, and cigarette consumption. Participants' trial-to-trial training choices were modeled using a reinforcement learning model that calculates separate learning rates associated with positive and negative prediction errors., Results: We found that learning from positive prediction error signals is reduced during smoking abstinence and enhanced following cigarette consumption. By contrast, learning from negative prediction error signals was enhanced during smoking abstinence and reduced following cigarette consumption. Finally, when tested with novel pairs of stimuli, participants were relatively better at selecting the positive feedback predicting stimuli than avoiding the negative feedback predicting stimuli during the smoking as usual session, a pattern that reversed following cigarette consumption., Conclusions: These findings provide a specific computational account of altered reinforcement learning induced by smoking state (abstinence and consumption) and may represent a unique target for treatment of nicotine addiction., Implications: This study illustrates the potential of computational psychiatry for understanding reinforcement learning deficits associated with substance use disorders in general and nicotine addiction in particular. We found that learning from positive prediction error signals is reduced during smoking abstinence and enhanced following cigarette consumption. By contrast, learning from negative prediction error signals was enhanced during smoking abstinence and reduced following cigarette consumption. By highlighting important computational differences between three states of smoking, these findings hold out promise for integrating experimental, computational, and theoretical analyses of decision-making function together with research on addiction-related disorders., (© The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objectives: Investigating biomarkers to demonstrate progression of Parkinson's disease (PD) is of high priority. We investigated the association of brain structural properties with progression of clinical outcomes and their ability to differentiate clinical subtypes of PD., Methods: A comprehensive set of clinical features was evaluated at baseline and 4.5-year follow-up for 144 de-novo PD patients from the Parkinson's Progression Markers Initiative. We created a global composite outcome (GCO) by combining z-scores of non-motor and motor symptoms, motor signs, overall activities of daily living and global cognition, as a single numeric indicator of prognosis. We classified patients into three subtypes based on multi-domain clinical criteria: 'mild motor-predominant', 'intermediate' and 'diffuse-malignant'. We analyzed diffusion-weighted scans at the early drug-naïve stage and extracted fractional anisotropy and mean diffusivity (MD) of basal ganglia and cortical sub-regions. Then, we employed graph theory to calculate network properties and used network-based statistic to investigate our primary hypothesis., Results: Baseline MD of globus pallidus was associated with worsening of motor severity, cognition, and GCO after 4.5 years of follow-up. Connectivity disruption at baseline was correlated with decline in cognition, and increase in GCO. Baseline MD of nucleus accumbens, globus pallidus and basal-ganglia were linked to clinical subtypes at 4.5-year of follow-up. Disruption in sub-cortical networks associated with being subtyped as 'diffuse-malignant' versus 'mild motor-predominant' after 4.5 years., Conclusions: Diffusion imaging analysis at the early de-novo stage of PD was able to differentiate clinical sub-types of PD after 4.5 years and was highly associated with future clinical outcomes of PD., Competing Interests: Declaration of Competing Interest None., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

It is becoming increasingly clear that brain network organization shapes the course and expression of neurodegenerative diseases. Parkinson disease (PD) is marked by progressive spread of atrophy from the midbrain to subcortical structures and, eventually, to the cerebral cortex. Recent discoveries suggest that the neurodegenerative process involves the misfolding and prion-like propagation of endogenous α-synuclein via axonal projections. However, the mechanisms that translate local "synucleinopathy" to large-scale network dysfunction and atrophy remain unknown. Here, we use an agent-based epidemic spreading model to integrate structural connectivity, functional connectivity, and gene expression and to predict sequential volume loss due to neurodegeneration. The dynamic model replicates the spatial and temporal patterning of empirical atrophy in PD and implicates the substantia nigra as the disease epicenter. We reveal a significant role for both connectome topology and geometry in shaping the distribution of atrophy. The model also demonstrates that SNCA and GBA transcription influence α-synuclein concentration and local regional vulnerability. Functional coactivation further amplifies the course set by connectome architecture and gene expression. Altogether, these results support the theory that the progression of PD is a multifactorial process that depends on both cell-to-cell spreading of misfolded proteins and regional vulnerability., Competing Interests: The authors have declared that no competing interests exist.

Although a significant genetic contribution to the risk of developing sporadic Parkinson's disease has been well described, the relationship between local genetic factors, pathogenesis, and subsequent spread of pathology throughout the brain has been largely unexplained in humans. To address this question, we use network diffusion modelling to infer probable pathology seed regions and patterns of disease spread from MRI atrophy maps derived from 232 de novo subjects in the Parkinson's Progression Markers Initiative study. Allen Brain Atlas regional transcriptional profiles of 67 Parkinson's disease risk factor genes were mapped to the inferred seed regions to determine the local influence of genetic risk factors. We used hierarchical clustering and L1 regularized regression analysis to show that transcriptional profiles of immune-related and lysosomal risk factor genes predict seed region location and the pattern of disease propagation from the most likely seed region, substantia nigra. By leveraging recent advances in transcriptomics, we show that regional microglial abundance quantified by high fidelity gene expression also predicts seed region location. These findings suggest that early disease sites are genetically susceptible to dysfunctional lysosomal α-synuclein processing and microglia-mediated neuroinflammation, which may initiate the disease process and contribute to spread of pathology along neural connectivity pathways., (© The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Obesity has been linked with subtle differences in brain structure. These differences tend to be especially relevant in prefrontal cortex regions, areas which play an important role in executive control. However, results in this field are often contradictory: although studies tend to report lower gray matter volume in relation to obesity, some have also observed null or positive associations. To overcome this issue, we conducted a meta-analysis on voxel-based morphometry (VBM) differences associated with obesity-related variables and validated the findings with an independent dataset., Methods: The literature search included combinations of the following key words: (i) neuroimaging terms: MRI, gray matter, brain, magnetic resonance; (ii) obesity-related terms: obesity, obese, body mass, waist circumference, adiposity. We conducted the meta-analysis using Anisotropic Effect-Size Seed-Based d Mapping (AES-SDM) software. Twenty-one studies on obesity and VBM fulfilled our inclusion criteria, representing 5882 participants (54% females) aged 18-92 years. To examine the validity of our meta-analytic results, we additionally tested on an independent dataset (Human Connectome Project, n = 378 participants) whether mean VBM values obtained for each cluster showed correlations with body mass index (BMI)., Results: We found that obesity-related variables were consistently associated with lower gray matter volume in areas including the medial prefrontal cortex, bilateral cerebellum, and left temporal pole. The clusters showed negative associations between gray matter volume and BMI in the independent dataset, with the exception of one cluster in the cerebellum., Conclusions: Our findings provide robust evidence that obesity and body mass are related to significantly lower gray matter volume in brain areas with a key role in executive control. These findings might suggest a neurobiological link between obesity and self-regulatory deficits.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a wide array of motor and non-motor symptoms. It remains unclear whether neurodegeneration in discrete loci gives rise to discrete symptoms, or whether network-wide atrophy gives rise to the unique behavioural and clinical profile associated with PD. Here we apply a data-driven strategy to isolate large-scale, multivariate associations between distributed atrophy patterns and clinical phenotypes in PD. In a sample of N = 229 de novo PD patients, we estimate disease-related atrophy using deformation based morphometry (DBM) of T1 weighted MR images. Using partial least squares (PLS), we identify a network of subcortical and cortical regions whose collective atrophy is associated with a clinical phenotype encompassing motor and non-motor features. Despite the relatively early stage of the disease in the sample, the atrophy pattern encompassed lower brainstem, substantia nigra, basal ganglia and cortical areas, consistent with the Braak hypothesis. In addition, individual variation in this putative atrophy network predicted longitudinal clinical progression in both motor and non-motor symptoms. Altogether, these results demonstrate a pleiotropic mapping between neurodegeneration and the clinical manifestations of PD, and that this mapping can be detected even in de novo patients., (Copyright © 2017. Published by Elsevier Inc.)

Enjoying music reliably ranks among life's greatest pleasures. Like many hedonic experiences, it engages several reward-related brain areas, with activity in the nucleus accumbens (NAc) most consistently reflecting the listener's subjective response. Converging evidence suggests that this activity arises from musical "reward prediction errors" (RPEs) that signal the difference between expected and perceived musical events, but this hypothesis has not been directly tested. In the present fMRI experiment, we assessed whether music could elicit formally modeled RPEs in the NAc by applying a well-established decision-making protocol designed and validated for studying RPEs. In the scanner, participants chose between arbitrary cues that probabilistically led to dissonant or consonant music, and learned to make choices associated with the consonance, which they preferred. We modeled regressors of trial-by-trial RPEs, finding that NAc activity tracked musically elicited RPEs, to an extent that explained variance in the individual learning rates. These results demonstrate that music can act as a reward, driving learning and eliciting RPEs in the NAc, a hub of reward- and music enjoyment-related activity., Competing Interests: The authors declare no conflict of interest.

Insufficient responses to hypocaloric diets have been attributed to hormonal adaptations that override self-control of food intake. We tested this hypothesis by measuring circulating energy-balance hormones and brain functional magnetic resonance imaging reactivity to food cues in 24 overweight/obese participants before, and 1 and 3 months after starting a calorie restriction diet. Increased activity and functional connectivity in prefrontal regions at month 1 correlated with weight loss at months 1 and 3. Weight loss was also correlated with increased plasma ghrelin and decreased leptin, and these changes were associated with food cue reactivity in reward-related brain regions. However, the reduction in leptin did not counteract weight loss; indeed, it was correlated with further weight loss at month 3. Activation in prefrontal regions associated with self-control could contribute to successful weight loss and maintenance. This work supports the role of higher-level cognitive brain function in body-weight regulation in humans., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Dopaminergic projections are hypothesized to stabilize neural signaling and neural representations, but how they shape regional information processing and large-scale network interactions remains unclear. Here we investigated effects of lowered dopamine levels on within-region temporal signal variability (measured by sample entropy) and between-region functional connectivity (measured by pairwise temporal correlations) in the healthy brain at rest. The acute phenylalanine and tyrosine depletion (APTD) method was used to decrease dopamine synthesis in 51 healthy participants who underwent resting-state functional MRI (fMRI) scanning. Functional connectivity and regional signal variability were estimated for each participant. Multivariate partial least squares (PLS) analysis was used to statistically assess changes in signal variability following APTD as compared with the balanced control treatment. The analysis captured a pattern of increased regional signal variability following dopamine depletion. Changes in hemodynamic signal variability were concomitant with changes in functional connectivity, such that nodes with greatest increase in signal variability following dopamine depletion also experienced greatest decrease in functional connectivity. Our results suggest that dopamine may act to stabilize neural signaling, particularly in networks related to motor function and orienting attention towards behaviorally-relevant stimuli. Moreover, dopamine-dependent signal variability is critically associated with functional embedding of individual areas in large-scale networks.