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Art therapy has emerged as a promising non-pharmacological, complementary approach in Parkinson’s Disease (PD), engaging motor, cognitive, and emotional systems and promoting neuroplasticity. However, individual responses to art therapy are highly variable, and predictors of therapeutic efficacy remain largely unknown. Here, we propose a novel framework combining brain fingerprinting and machine learning to predict art therapy outcomes in PD. We mapped functional connectomes from restingstate functional MRI (fMRI) of PD patients before and after art therapy, assessed individual connectome-based fingerprints, and examined their spatial specificity. First, we found that functional connectomes derived from resting-state fMRI retained high levels of identifiability in both healthy controls and patients with PD, indicating that stable and subject-specific brain fingerprints are preserved even in the context of neurodegeneration. Second, we identified associations between fingerprint topography and cognitive domains relevant to PD. Although mean edge-wise reliability was comparable across conditions, the topography of reliability differed by group and session. In PD, reliable edges shifted toward posterior midline and occipital territories, with fewer stable connections in several associative systems. This pattern suggests a shift in reliability toward sensory-perceptual hubs and away from networks that support flexible control. Finally, leveraging network fingerprints, we computed subject-wise topology measures which served as input to a supervised classification framework designed to predict clinical responder versus non-responder status, based on changes in UPDRS-III scores. Among the tested classifiers, treebased methods provided the most robust predictive performance, with random forest achieving the highest performance, with an accuracy of 0.83 and a ROC-AUC of 0.80. Our results demonstrate that brain fingerprint-informed network measures capture interindividual variability in art therapy response, offering a datadriven, personalized approach to rehabilitation. This study provides the first evidence that functional connectome fingerprints can guide art therapy interventions, thus opening a new precision medicine framework in PD.

Increasing evidence indicates that catecholaminergic degeneration, especially in the ventral tegmental area (VTA) and locus coeruleus (LC), precedes classical Alzheimer’s disease (AD) pathology, a finding confirmed by structural and functional imaging in patient cohorts with amnestic MCI and AD. Given the central role of these nuclei in shaping motivation, arousal and memory, we developed a dedicated pipeline to reconstruct the catecholaminergic connectome at the whole-brain level. Tg2576 mice overexpressing human APP695 with the Swedish mutation were employed. Brains were cleared, immunolabeled for tyrosine hydroxylase (TH), and imaged using volumetric light-sheet microscopy. Tiles were stitched with BigStitcher, and Arivis Pro U-Net models segmented soma, axon hillocks, dendrites, and nuclei. 3D reconstruction and automated tracing were performed with Vaa3D APP2. Reconstructions were registered to the Allen Brain Atlas. NetworkX was used for data analysis, and in MATLAB the physiological laws of CNS impulse transmission were applied to the networks to perform simulations. The pipeline generated a whole-brain catecholaminergic connectomic model for Tg2576 and controls. A decrease in TH+ neuron counts was observed in both VTA and LC, leading to marked denervation of the hippocampus, amygdala, medial prefrontal cortex, and the entire limbic lobe. Alterations were also noted within and in between catecholaminergic nuclei, with changes in their intrinsic functional circuit units, modifications of dendritic arborizations, and a reduction in reverberant circuits that normally sustain continuous output activity. conclusions: This represents the first connectomic model of catecholaminergic architecture in both healthy and AD brains. TH+ fibers normally synapse onto GABAergic neurons; their denervation drives excitotoxicity and degeneration in target areas. These alterations may underlie prodromal psychiatric symptoms and, through hippocampal and cortical denervation, may be the cause of pathological alterations and subsequent cognitive decline.

Inhibitory neurons in mammalian cortex exhibit diverse physiological, morphological, molecular, and connectivity signatures. While considerable work has measured the average connectivity of several interneuron classes, there remains a fundamental lack of understanding of the connectivity distribution of distinct inhibitory cell types with synaptic resolution, how it relates to properties of target cells, and how it affects function. Here, we used large-scale electron microscopy and functional imaging to address these questions for chandelier cells in layer 2/3 of the mouse visual cortex. With dense reconstructions from electron microscopy, we mapped the complete chandelier input onto 153 pyramidal neurons. We found that synapse number is highly variable across the population and is correlated with several structural features of the target neuron. This variability in the number of axo-axonic ChC synapses is higher than the variability seen in perisomatic inhibition. Biophysical simulations show that the observed pattern of axo-axonic inhibition is particularly effective in controlling excitatory output when excitation and inhibition are co-active. Finally, we measured chandelier cell activity in awake animals using a cell-type-specific calcium imaging approach and saw highly correlated activity across chandelier cells. In the same experiments, in vivo chandelier population activity correlated with pupil dilation, a proxy for arousal. Together, these results suggest that chandelier cells provide a circuit-wide signal whose strength is adjusted relative to the properties of target neurons.

•We related age-related cortical thickness differences with indices of brain network architecture in a surface-based spatial correlation analysis of a large population-based sample.•Age effects on cortical thickness were strongest in sensorimotor areas.•Regional age-related differences were strongly correlated within the structurally defined node neighborhood.•The overall pattern of thickness differences was found to be anchored in the functional network hierarchy as encoded by macroscale functional connectivity gradients.•Taken together, we demonstrate a link between functional and structural brain network topology and age effects on cortical morphology. Age-related cortical atrophy, approximated by cortical thickness measurements from magnetic resonance imaging, follows a characteristic pattern over the lifespan. Although its determinants remain unknown, mounting evidence demonstrates correspondence between the connectivity profiles of structural and functional brain networks and cortical atrophy in health and neurological disease. Here, we performed a cross-sectional multimodal neuroimaging analysis of 2633 individuals from a large population-based cohort to characterize the association between age-related differences in cortical thickness and functional as well as structural brain network topology. We identified a widespread pattern of age-related cortical thickness differences including “hotspots” of pronounced age effects in sensorimotor areas. Regional age-related differences were strongly correlated within the structurally defined node neighborhood. The overall pattern of thickness differences was found to be anchored in the functional network hierarchy as encoded by macroscale functional connectivity gradients. Lastly, the identified difference pattern covaried significantly with cognitive and motor performance. Our findings indicate that connectivity profiles of functional and structural brain networks act as organizing principles behind age-related cortical thinning as an imaging surrogate of cortical atrophy.

The Human Connectome Projects in Development (HCP-D) and Aging (HCP-A) are two large-scale brain imaging studies that will extend the recently completed HCP Young-Adult (HCP-YA) project to nearly the full lifespan, collecting structural, resting-state fMRI, task-fMRI, diffusion, and perfusion MRI in participants from 5 to 100+ years of age. HCP-D is enrolling 1300+ healthy children, adolescents, and young adults (ages 5–21), and HCP-A is enrolling 1200+ healthy adults (ages 36–100+), with each study collecting longitudinal data in a subset of individuals at particular age ranges. The imaging protocols of the HCP-D and HCP-A studies are very similar, differing primarily in the selection of different task-fMRI paradigms. We strove to harmonize the imaging protocol to the greatest extent feasible with the completed HCP-YA (1200+ participants, aged 22–35), but some imaging-related changes were motivated or necessitated by hardware changes, the need to reduce the total amount of scanning per participant, and/or the additional challenges of working with young and elderly populations. Here, we provide an overview of the common HCP-D/A imaging protocol including data and rationales for protocol decisions and changes relative to HCP-YA. The result will be a large, rich, multi-modal, and freely available set of consistently acquired data for use by the scientific community to investigate and define normative developmental and aging related changes in the healthy human brain. •The Lifespan HCP in Development and Aging aim to characterize brain connectivity changes in human childhood and aging.•Here, we report on the multimodal brain imaging protocol being used for both projects.•We report pilot and ancillary data that informed the selection of hardware and imaging parameters.•The resulting data will be publicly released in unprocessed and minimally processed formats.

•Functional connectivity (FC) analysis has been widely applied in clinical populations.•Intrinsic FC networks are found in virtually all brains.•FC suggests that disorders and diseases are less ‘focal’ than previously believed.•Widespread FC changes are found in non-communicative patients.•Challenging barriers must be overcome to enable further clinical applications. Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.

The development of human cognition results from the emergence of coordinated activity between distant brain areas. Network science, combined with non-invasive functional imaging, has generated unprecedented insights regarding the adult brain's functional organization, and promises to help elucidate the development of functional architectures supporting complex behavior. Here we review what is known about functional network development from birth until adulthood, particularly as understood through the use of resting-state functional connectivity MRI (rs-fcMRI). We attempt to synthesize rs-fcMRI findings with other functional imaging techniques, with macro-scale structural connectivity, and with knowledge regarding the development of micro-scale structure. We highlight a number of outstanding conceptual and technical barriers that need to be addressed, as well as previous developmental findings that may need to be revisited. Finally, we discuss key areas ripe for future research in order to (1) better characterize normative developmental trajectories, (2) link these trajectories to biologic mechanistic events, as well as component behaviors and (3) better understand the clinical implications and pathophysiological basis of aberrant network development. •Reviews development of functional connectivity networks from birth until adulthood.•Reviews trends in resting-state functional MR imaging (rs-fMRI) and network analysis.•Synthesizes developmental rs-fMRI findings with structural connectivity and EEG/MEG.•Suggests strategies to overcome limitations of rs-fMRI in developmental studies.•Suggests approaches to interrogate neurodevelopmental disorders.

•We review the developments that led to a connectomic deep brain stimulation framework.•We outline eight possibilities offered by this framework including predictive models and guidance for deep brain stimulation programming.•We present methods to investigate networks associated with clinical improvements or the occurrence of side effects. The process of altering neural activity – neuromodulation – has long been used to treat patients with brain disorders and answer scientific questions. Deep brain stimulation in particular has provided clinical benefit to over 150,000 patients. However, our understanding of how neuromodulation impacts the brain is evolving. Instead of focusing on the local impact at the stimulation site itself, we are considering the remote impact on brain regions connected to the stimulation site. Brain connectivity information derived from advanced magnetic resonance imaging data can be used to identify these connections and better understand clinical and behavioral effects of neuromodulation. In this article, we review studies combining neuromodulation and brain connectomics, highlighting opportunities where this approach may prove particularly valuable. We focus on deep brain stimulation, but show that the same principles can be applied to other forms of neuromodulation, such as transcranial magnetic stimulation and MRI-guided focused ultrasound. We outline future perspectives and provide testable hypotheses for future work.

Dystonia is a debilitating disease with few treatment options. One effective option is deep brain stimulation (DBS) to the internal pallidum. While cervical and generalized forms of isolated dystonia have been targeted with a common approach to the posterior third of the nucleus, large-scale investigations regarding optimal stimulation sites and potential network effects have not been carried out. Here, we retrospectively studied clinical results following DBS for cervical and generalized dystonia in a multicenter cohort of 80 patients. We model DBS electrode placement based on pre- and postoperative imaging and introduce an approach to map optimal stimulation sites to anatomical space. Second, we investigate which tracts account for optimal clinical improvements, when modulated. Third, we investigate distributed stimulation effects on a whole-brain functional connectome level. Our results show marked differences of optimal stimulation sites that map to the somatotopic structure of the internal pallidum. While modulation of the striatopallidofugal axis of the basal ganglia accounted for optimal treatment of cervical dystonia, modulation of pallidothalamic bundles did so in generalized dystonia. Finally, we show a common multisynaptic network substrate for both phenotypes in the form of connectivity to the cerebellum and somatomotor cortex. Our results suggest a brief divergence of optimal stimulation networks for cervical vs. generalized dystonia within the pallidothalamic loop that merge again on a thalamo-cortical level and share a common whole-brain network.

The original Human Connectome Project yielded a rich data set on structural and functional connectivity in a large sample of healthy young adults using improved methods of data acquisition, analysis, and sharing. More recent efforts are extending this approach to include infants, children, older adults, and brain disorders. This paper introduces and describes the Human Connectome Project in Aging (HCP-A), which is currently recruiting 1200 + healthy adults aged 36 to 100+, with a subset of 600 + participants returning for longitudinal assessment. Four acquisition sites using matched Siemens Prisma 3T MRI scanners with centralized quality control and data analysis are enrolling participants. Data are acquired across multimodal imaging and behavioral domains with a focus on factors known to be altered in advanced aging. MRI acquisitions include structural (whole brain and high resolution hippocampal) plus multiband resting state functional (rfMRI), task fMRI (tfMRI), diffusion MRI (dMRI), and arterial spin labeling (ASL). Behavioral characterization includes cognitive (such as processing speed and episodic memory), psychiatric, metabolic, and socioeconomic measures as well as assessment of systemic health (with a focus on menopause via hormonal assays). This dataset will provide a unique resource for examining how brain organization and connectivity changes across typical aging, and how these differences relate to key characteristics of aging including alterations in hormonal status and declining memory and general cognition. A primary goal of the HCP-A is to make these data freely available to the scientific community, supported by the Connectome Coordination Facility (CCF) platform for data quality assurance, preprocessing and basic analysis, and shared via the NIMH Data Archive (NDA). Here we provide the rationale for our study design and sufficient details of the resource for scientists to plan future analyses of these data. A companion paper describes the related Human Connectome Project in Development (HCP-D, Somerville et al., 2018), and the image acquisition protocol common to both studies (Harms et al., 2018). •The Lifespan Human Connectome Project-Aging (HCP-A) project is collecting multimodal MRI and behavioral data from 1200 + participants aged 36–100+.•MRI includes structural, resting state fMRI, task fMRI, diffusion, and arterial spin labeled imaging.•Bio-behavioral assessments include cognitive, psychiatric, metabolic, socioeconomic, and systemic health characterization.•600 + participants will receive a longitudinal follow-up at 20–24 months.•These data will become a public resource to enable in-depth studies of typical brain aging.