Connectome embedding in multidimensional graph spaces.

Connectomes' topological organization can be quantified using graph theory. Here, we investigated brain networks in higher dimensional spaces defined by up to 10 graph theoretic nodal properties. These properties assign a score to nodes, reflecting their meaning in the network.

Comparing structure-function relationships in brain networks using EEG and fNIRS.

Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure-functional relationship has yet to be explored. In our study, using simultaneous Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects, we characterize global and local structure-function coupling using source-reconstructed EEG and fNIRS signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored.

Fingerprints of brain disease: connectome identifiability in Alzheimer's disease.

Functional connectivity patterns in the human brain, like the friction ridges of a fingerprint, can uniquely identify individuals. Does this "brain fingerprint" remain distinct even during Alzheimer's disease (AD)? Using fMRI data from healthy and pathologically ageing subjects, we find that individual functional connectivity profiles remain unique and highly heterogeneous during mild cognitive impairment and AD. However, the patterns that make individuals identifiable change with disease progression, revealing a reconfiguration of the brain fingerprint.

Functional network centrality indicates interactions between APOE4 and age across the clinical spectrum of Alzheimer's Disease.

Advanced age is the most important risk factor for Alzheimer's disease (AD), and carrier-status of the Apolipoprotein E4 (APOE4) allele is the strongest known genetic risk factor. Many studies have consistently shown a link between APOE4 and synaptic dysfunction, possibly reflecting pathologically accelerated biological aging in persons at risk for AD. To test the hypothesis that distinct functional connectivity patterns characterize APOE4 carriers across the clinical spectrum of AD, we investigated 128 resting state functional Magnetic Resonance Imaging (fMRI) datasets from the Alzheimer's Disease Neuroimaging Initiative database (ADNI), representing all disease stages from cognitive normal to clinical dementia.

Longitudinal Analysis of Brain Function-Structure Dependencies in 22q11.2 Deletion Syndrome and Psychotic Symptoms.

Background: Compared with conventional unimodal analysis, understanding how brain function and structure relate to one another opens a new biologically relevant assessment of neural mechanisms. However, how function-structure dependencies (FSDs) evolve throughout typical and abnormal neurodevelopment remains elusive. The 22q11.

The hippocampus dissociates present from past and future goals.

Our brain adeptly navigates goals across time frames, distinguishing between urgent needs and those of the past or future. The hippocampus is a region known for supporting mental time travel and organizing information along its longitudinal axis, transitioning from detailed posterior representations to generalized anterior ones. This study investigates the role of the hippocampus in distinguishing goals over time: whether the hippocampus encodes time regardless of detail or abstraction, and whether the hippocampus preferentially activates its anterior region for temporally distant goals (past and future) and its posterior region for immediate goals.

Linking connectivity of deep brain stimulation of nucleus accumbens area with clinical depression improvements: a retrospective longitudinal case series.

Treatment-resistant depression is a severe form of major depressive disorder and deep brain stimulation is currently an investigational treatment. The stimulation's therapeutic effect may be explained through the functional and structural connectivities between the stimulated area and other brain regions, or to depression-associated networks. In this longitudinal, retrospective study, four female patients with treatment-resistant depression were implanted for stimulation in the nucleus accumbens area at our center.

Hippocampal timestamp for goals.

Our brain must manage multiple goals that differ in their temporal proximity. Some goals require immediate attention, while others have already been accomplished, or will be relevant later in time. Here, we examined how the hippocampus represents the temporal distance to different goals using a novel space-themed paradigm during 7T functional MRI (n=31).

Prediction of post-stroke motor recovery benefits from measures of sub-acute widespread network damages.

Following a stroke in regions of the brain responsible for motor activity, patients can lose their ability to control parts of their body. Over time, some patients recover almost completely, while others barely recover at all. It is known that lesion volume, initial motor impairment and cortico-spinal tract asymmetry significantly impact motor changes over time.

On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI.

Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP).

Neural substrates of psychosis revealed by altered dependencies between brain activity and white-matter architecture in individuals with 22q11 deletion syndrome.

Background: Dysconnectivity has been consistently proposed as a major key mechanism in psychosis. Indeed, disruptions in large-scale structural and functional brain networks have been associated with psychotic symptoms. However, brain activity is largely constrained by underlying white matter pathways and the study of function-structure dependency, compared to conventional unimodal analysis, allows a biologically relevant assessment of neural mechanisms.

Mapping of Structure-Function Age-Related Connectivity Changes on Cognition Using Multimodal MRI.

The relationship between age-related changes in brain structural connectivity (SC) and functional connectivity (FC) with cognition is not well understood. Furthermore, it is not clear whether cognition is represented a similar spatial pattern of FC and SC or instead is mapped by distinct sets of distributed connectivity patterns. To this end, we used a longitudinal, within-subject, multimodal approach aiming to combine brain data from diffusion-weighted MRI (DW-MRI), and functional MRI (fMRI) with behavioral evaluation, to better understand how changes in FC and SC correlate with changes in cognition in a sample of older adults.

CSF tap test in idiopathic normal pressure hydrocephalus: still a necessary prognostic test?

Objective: To assess whether gait, neuropsychological, and multimodal MRI parameters predict short-term symptom reversal after cerebrospinal fluid (CSF) tap test in idiopathic normal pressure hydrocephalus (iNPH).

Methods: Thirty patients (79.3 ± 5.

Brain structure-function coupling provides signatures for task decoding and individual fingerprinting.

Brain signatures of functional activity have shown promising results in both decoding brain states, meaning distinguishing between different tasks, and fingerprinting, that is identifying individuals within a large group. Importantly, these brain signatures do not account for the underlying brain anatomy on which brain function takes place. Structure-function coupling based on graph signal processing (GSP) has recently revealed a meaningful spatial gradient from unimodal to transmodal regions, on average in healthy subjects during resting-state.

When makes you unique: Temporality of the human brain fingerprint.

The extraction of “fingerprints” from human brain connectivity data has become a new frontier in neuroscience. However, the time scales of human brain identifiability are still largely unexplored. We here investigate the dynamics of brain fingerprints along two complementary axes: (i) What is the optimal time scale at which brain fingerprints integrate information and (ii) when best identification happens.

Altered anterior default mode network dynamics in progressive multiple sclerosis.

Background: Modifications in brain function remain relatively unexplored in progressive multiple sclerosis (PMS), despite their potential to provide new insights into the pathophysiology of the disease at this stage.

Objectives: To characterize the dynamics of functional networks at rest in patients with PMS, and the relation with clinical disability.

Methods: Thirty-two patients with PMS underwent clinical and cognitive assessment.

Dynamics of amygdala connectivity in bipolar disorders: a longitudinal study across mood states.

Alterations in activity and connectivity of brain circuits implicated in emotion processing and emotion regulation have been observed during resting-state for different clinical phases of bipolar disorders (BD), but longitudinal investigations across different mood states in the same patients are still rare. Furthermore, measuring dynamics of functional connectivity patterns offers a powerful method to explore changes in the brain's intrinsic functional organization across mood states. We used a novel co-activation pattern (CAP) analysis to explore the dynamics of amygdala connectivity at rest in a cohort of 20 BD patients prospectively followed-up and scanned across distinct mood states: euthymia (20 patients; 39 sessions), depression (12 patients; 18 sessions), or mania/hypomania (14 patients; 18 sessions).

Generative Adversarial Networks Improve the Reproducibility and Discriminative Power of Radiomic Features.

Purpose: To assess the contribution of a generative adversarial network (GAN) to improve intermanufacturer reproducibility of radiomic features (RFs).

Materials And Methods: The authors retrospectively developed a cycle-GAN to translate texture information from chest radiographs acquired using one manufacturer (Siemens) to chest radiographs acquired using another (Philips), producing fake chest radiographs with different textures. The authors prospectively evaluated the ability of this texture-translation cycle-GAN to reduce the intermanufacturer variability of RFs extracted from the lung parenchyma.

Tapping into Multi-Faceted Human Behavior and Psychopathology Using fMRI Brain Dynamics.

Human behavior comprises many aspects that stand out by their dynamic nature. To quantify its neural underpinnings, time-resolved fMRI methods have blossomed over the past decade. In this review we conceptually organize a broad repertoire of dynamic analytical pipelines and extract general observations on their application to the study of behavior and brain disorders.

Decoupling of brain function from structure reveals regional behavioral specialization in humans.

The brain is an assembly of neuronal populations interconnected by structural pathways. Brain activity is expressed on and constrained by this substrate. Therefore, statistical dependencies between functional signals in directly connected areas can be expected higher.

Guided graph spectral embedding: Application to the connectome.

Graph spectral analysis can yield meaningful embeddings of graphs by providing insight into distributed features not directly accessible in nodal domain. Recent efforts in graph signal processing have proposed new decompositions-for example, based on wavelets and Slepians-that can be applied to filter signals defined on the graph. In this work, we take inspiration from these constructions to define a new guided spectral embedding that combines maximizing energy concentration with minimizing modified embedded distance for a given importance weighting of the nodes.

Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal I-FP-CIT indices.

Objectives: To provide an automated classification method for degenerative parkinsonian syndromes (PS) based on semiquantitative I-FP-CIT SPECT striatal indices and support-vector-machine (SVM) analysis.

Methods: I-FP-CIT SPECT was performed at a single-center level on 370 individuals with PS, including 280 patients with Parkinson's disease (PD), 21 with multiple system atrophy-parkinsonian type (MSA-P), 41 with progressive supranuclear palsy (PSP) and 28 with corticobasal syndrome (CBS) (mean age 70.3 years, 47% female, mean disease duration at scan 1.

Dynamics of functional connectivity at high spatial resolution reveal long-range interactions and fine-scale organization.

Dynamic functional connectivity (dFC) derived from resting-state functional magnetic resonance imaging sheds light onto moment-to-moment reconfigurations of large-scale functional brain networks. Due to computational limits, connectivity is typically computed using pre-defined atlases, a non-trivial choice that might influence results. Here, we leverage new computational methods to retrieve dFC at the voxel level in terms of dominant patterns of fluctuations, and demonstrate that this new representation is informative to derive meaningful brain parcellations, capturing both long-range interactions and fine-scale local organization.

The dynamic functional connectome: State-of-the-art and perspectives.

Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e.