BACKGROUND AND OBJECTIVES:Choroid plexus (ChP) is emerging as a key brain structure in the pathophysiology of neurodegenerative disorders. In this observational study, we investigated ChP volume in a large cohort of patients with frontotemporal lobar degeneration (FTLD) spectrum to explore a possible link between ChP volume and other disease-specific biomarkers.METHODS:Participants included patients meeting clinical criteria for a probable syndrome in the FTLD spectrum. Structural brain MRI imaging, serum neurofilament light (NfL), serum phosphorylated-Tau181 (p-Tau181), and cognitive and behavioral data were collected. MRI ChP volumes were obtained from an ad-hoc segmentation model based on a Gaussian Mixture Models algorithm.RESULTS:Three-hundred and sixteen patients within FTLD spectrum were included in this study, specifically 135 patients diagnosed with behavioral variant frontotemporal dementia (bvFTD), 75 primary progressive aphasia, 46 progressive supranuclear palsy, and 60 corticobasal syndrome. In addition, 82 age-matched healthy participants were recruited as controls (HCs). ChP volume was significantly larger in patients with FTLD compared with HC, across the clinical subtype. Moreover, we found a significant difference in ChP volume between HC and patients stratified for disease-severity based on CDR plus NACC FTLD, including patients at very early stage of the disease. Interestingly, ChP volume correlated with serum NfL, cognitive/behavioral deficits, and with patterns of cortical atrophy. Finally, ChP volume seemed to discriminate HC from patients with FTLD better than other previously identified brain structure volumes.DISCUSSION:Considering the clinical, pathologic, and genetic heterogeneity of the disease, ChP could represent a potential biomarker across the FTLD spectrum, especially at the early stage of disease. Further longitudinal studies are needed to establish its role in disease onset and progression.CLASSIFICATION OF EVIDENCE:This study provides Class III evidence that choroid plexus volume, as measured on MRI scan, can assist in differentiating patients with FTLD from healthy controls and in characterizing disease severity.

Recent studies have synchronized transcranial magnetic stimulation (TMS) application with pre‐defined brain oscillatory phases showing how brain response to perturbation depends on the brain state. However, none have investigated whether phase‐dependent TMS can possibly modulate connectivity with homologous distant brain regions belonging to the same network. In the framework of network‐targeted TMS, we investigated whether stimulation delivered at a specific phase of ongoing brain oscillations might favour stronger cortico‐cortical (c‐c) synchronization of distant network nodes connected to the stimulation target. Neuronavigated TMS pulses were delivered over the primary motor cortex (M1) during ongoing electroencephalography recording in 24 healthy individuals over two repeated sessions 1 month apart. Stimulation effects were analysed considering whether the TMS pulse was delivered at the time of a positive (peak) or negative (trough) phase of μ‐frequency oscillation, which determines c‐c synchrony within homologous areas of the sensorimotor network. Diffusion weighted imaging was used to study c‐c connectivity within the sensorimotor network and identify contralateral regions connected with the stimulation spot. Depending on when during the μ‐activity the TMS‐pulse was applied (peak or trough), its impact on inter‐hemispheric network synchrony varied significantly. Higher M1–M1 phase‐lock synchronization after the TMS‐pulse (0–200 ms) in the μ‐frequency band was found for trough compared to peak stimulation trials in both study visits. Phase‐dependent TMS delivery might be crucial not only to amplify local effects but also to increase the magnitude and reliability of the response to the external perturbation, with implications for interventions aimed at engaging more distributed functional brain networks.

Combining Transcranial Magnetic Stimulation (TMS) with electroencephalography (EEG) offers the opportunity to study signal propagation dynamics at high temporal resolution in the human brain. TMS pulse induces a local effect which propagates across cortical networks engaging distant cortical and subcortical sites. However, the degree of propagation supported by the structural compared to functional connectome remains unclear. Clarifying this issue would help tailor TMS interventions to maximize target engagement. The goal of this study was to establish the contribution of functional and structural connectivity in predicting TMSinduced signal propagation after perturbation of two distinct brain networks. For this purpose, 24 healthy individuals underwent two identical TMS-EEG visits where neuronavigated TMS pulses were delivered to nodes of the default mode network (DMN) and the dorsal attention network (DAN). The functional and structural connectivity derived from each individual stimulation spot were characterized via functional magnetic resonance imaging (fMRI) and Diffusion Weighted Imaging (DWI), and signal propagation across these two metrics was compared. Direct comparison between the signal extracted from brain regions either functionally or structurally connected to the stimulation sites, shows a stronger activation over cortical areas connected via white matter pathways, with a minor contribution of functional projections. This pattern was not observed when analyzing spontaneous resting state EEG activity. Overall, results suggest that structural links can predict network-level response to perturbation more accurately than functional connectivity. Additionally, DWI-based estimation of propagation patterns can be used to estimate off-target engagement of other networks and possibly guide target selection to maximize specificity.

BackgroundIn recent years, it has become increasingly apparent that characterizing individual brain structure, connectivity and dynamics is essential for understanding brain function in health and disease. However, the majority of neuroimaging and brain stimulation research has characterized human brain function by averaging measurements from groups of subjects and providing population-level inferences. External perturbations applied directly to well-defined brain regions can reveal distinctive information about the state, connectivity and dynamics of the human brain at the individual level.ObjectivesIn a series of studies, we aimed to characterize individual brain responses to MRI-guided transcranial magnetic stimulation (TMS), and explore the reproducibility of the evoked effects, differences between brain regions, and their individual specificity.MethodsIn the first study, we administered single pulses of TMS to both anatomically (left dorsolateral prefrontal cortex- 'L-DLPFC', left Intra-parietal lobule- 'L-IPL) and functionally (left motor cortex- 'L-M1', right default mode network- 'R-DMN, right dorsal attention network- 'R-DAN') defined cortical nodes in the frontal, motor, and parietal regions across two identical sessions spaced one month apart in 24 healthy volunteers. In the second study, we extended our analyses to two independent data sets (n = 10 in both data sets) having different sham-TMS protocols.ResultsIn the first study, we found that perturbation-induced cortical propagation patterns are heterogeneous across individuals but highly reproducible within individuals, specific to the stimulated region, and distinct from spontaneous activity. Most importantly, we demonstrate that by assessing the spatiotemporal characteristics of TMS-induced brain responses originating from different cortical regions, individual subjects can be identified with perfect accuracy. In the second study, we demonstrated that subject specificity of TEPs is generalizable across independent data sets and distinct from non-transcranial neural responses evoked by sham-TMS protocols.ConclusionsPerturbation-induced brain responses reveal unique "brain fingerprints" that reflect causal connectivity dynamics of the stimulated brain regions, and may serve as reliable biomarkers of individual brain function.

Several decades of research suggest that weak electric fields may influence neural processing, including those induced by neuronal activity and proposed as a substrate for a potential new cellular communication system, i.e., ephaptic transmission. Here we aim to model mesoscopic ephaptic activity in the human brain and explore its trajectory during aging by characterizing the electric field generated by cortical dipoles using realistic finite element modeling. Extrapolating from electrophysiological measurements, we first observe that modeled endogenous field magnitudes are comparable to those in measurements of weak but functionally relevant self-generated fields and to those produced by noninvasive transcranial brain stimulation, and therefore possibly able to modulate neuronal activity. Then, to evaluate the role of these fields in the human cortex in large MRI databases, we adapt an interaction approximation that considers the relative orientation of neuron and field to estimate the membrane potential perturbation in pyramidal cells. We use this approximation to define a simplified metric (EMOD1) that weights dipole coupling as a function of distance and relative orientation between emitter and receiver and evaluate it in a sample of 401 realistic human brain models from healthy subjects aged 16-83. Results reveal that ephaptic coupling, in the simplified mesoscopic modeling approach used here, significantly decreases with age, with higher involvement of sensorimotor regions and medial brain structures. This study suggests that by providing the means for fast and direct interaction between neurons, ephaptic modulation may contribute to the complexity of human function for cognition and behavior, and its modification across the lifespan and in response to pathology.

Human intelligence can be broadly subdivided into fluid (gf) and crystallized (gc) intelligence, each tapping into distinct cognitive abilities. Although neuroanatomical correlates of intelligence have been previously studied, differential contribution of cortical morphologies to gf and gc has not been fully delineated. Here, we tried to disentangle the contribution of cortical thickness, cortical surface area, and cortical gyrification to gf and gc in a large sample of healthy young subjects (n = 740, Human Connectome Project) with high-resolution MRIs, followed by replication in a separate data set with distinct cognitive measures indexing gf and gc. We found that while gyrification in distributed cortical regions had positive association with both gf and gc, surface area and thickness showed more regional associations. Specifically, higher performance in gf was associated with cortical expansion in regions related to working memory, attention, and visuo-spatial processing, while gc was associated with thinner cortex as well as higher cortical surface area in language-related networks. We discuss the results in a framework where "horizontal" cortical expansion enables higher resource allocation, computational capacity, and functional specificity relevant to gf and gc, while lower cortical thickness possibly reflects cortical pruning facilitating "vertical" intracolumnar efficiency in knowledge-based tasks relevant mostly to gc.

Large-scale brain networks are often described using resting-state functional magnetic resonance imaging (fMRI). However, the blood oxygenation level-dependent (BOLD) signal provides an indirect measure of neuronal firing and reflects slow-evolving hemodynamic activity that fails to capture the faster timescale of normal physiological function. Here we used fMRI-guided transcranial magnetic stimulation (TMS) and simultaneous electroencephalography (EEG) to characterize individual brain dynamics within discrete brain networks at high temporal resolution. TMS was used to induce controlled perturbations to individually defined nodes of the default mode network (DMN) and the dorsal attention network (DAN). Source-level EEG propagation patterns were network-specific and highly reproducible across sessions 1 month apart. Additionally, individual differences in high-order cognitive abilities were significantly correlated with the specificity of TMS propagation patterns across DAN and DMN, but not with resting-state EEG dynamics. Findings illustrate the potential of TMS-EEG perturbation-based biomarkers to characterize network-level individual brain dynamics at high temporal resolution, and potentially provide further insight on their behavioral significance.

Fronto-parietal activity has been related to fluid intelligence and flexible cognitive control. However, causal insights on this relation are lacking. We used real-time integration of MRI-guided TMS and EEG to characterize the spatial and temporal properties of signal propagation between these two regions and relate them to cognitive performance.31 healthy adults (55 ±6 years, 20 female) underwent TMS-EEG and a full cognitive assessment. Local and propagated current from 5 source space-reconstructed scouts ipsilateral to two stimulation sites (pre frontal cortex (PFC) and inferior parietal lobule (IPL)) was quantified in two-time windows (15-40ms and 40-80ms) and related to domain-general (global cognition) and domain-specific (memory, working memory, reasoning, flexibility, lexical access and visuo-spatial) cognitive functions.TMS-evoked activity from stimulation of the PFC and the IPL resulted in local and distributed activity across frontoparietal regions. TMS-evoked activity in local regions was not correlated with cognitive functions. In response to TMS of the PFC, propagated current to the distal superior parietal scout in the first 15-40ms was significantly associated with global cognition (β = 2.63, SE = .898, p = .008, R2 = .31). Similarly, following TMS of the IPL, propagation to the middle prefrontal gyrus scout (15-40ms) was significantly associated with global cognition (β = 2.67, SE = 1.289, p = .025, R2 = .27). In an exploratory step, domain-specific correlations were seen in the PFC condition.Locally evoked activity measured via source space reconstruction from TMS of two association hubs is not associated with cognitive functions. However, the propagation of the TMS pulse through frontoparietal connections is associated with overall cognitive ability. These associations are driven by a number of cognitive domains in the PFC stimulation condition.### Competing Interest StatementDr. A. Pascual-Leone serves on the scientific advisory boards for Starlab Neuroscience, Neuroelectrics, Magstim, Magventure, and Nexstim; is co-founder of Linus Health; and is listed as an inventor on several issued and pending patents on the real-time integration of transcranial magnetic stimulation with electroencephalography and magnetic resonance imaging. All other authors disclose no conflicts of interest including no financial, personal, or other relationships with other people or organizations that could inappropriately influence this work

The choroid plexus (ChP) is a major source of cerebrospinal fluid (CSF) production, with a direct and indirect role in protein clearance, and pathogenesis of Alzheimer's disease (AD). Here, we tested the link between the ChP volume and levels of CSF proteins in 2 data sets of (i) healthy controls, mild cognitive impairment (MCI), and AD patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 509), and (ii) healthy controls and Parkinson's disease (PD) patients from the Parkinson's Progression Markers Initiative (N = 302). All patients had baseline CSF proteins (amyloid-β, total and phosphorylated-tau and α-synuclein (only in Parkinson's Progression Markers Initiative)). ChP was automatically segmented on 3T structural T1-weighted MRIs. We found negative associations between ChP volume and CSF proteins, which were stronger in healthy controls, early-MCI patients, and PD patients compared with late-MCI and AD patients. Further grouping of patients of ADNI dataset into amyloid-positive and amyloid-negative based on their florbetapir (AV45) PET imaging showed that the association between ChP volume and CSF proteins (t/p-tau) was lower in amyloid-positive group. Our findings support the possible role of ChP in the clearance of CSF proteins, provide evidence for ChP dysfunction in AD, and suggest the need to account for the ChP volume in future studies of CSF-based biomarkers.

Information processing in the brain is mediated by structural white matter pathways and is highly dependent on topological brain properties. Here we combined transcranial magnetic stimulation (TMS) with high-density electroencephalography (EEG) and Diffusion Weighted Imaging (DWI), specifically looking at macroscale connectivity to understand whether regional, network-level or whole-brain structural properties are more responsible for stimulus propagation. Neuronavigated TMS pulses were delivered over two individually defined nodes of the default mode (DMN) and dorsal attention (DAN) networks in a group of healthy subjects, with test-retest reliability assessed 1-month apart. TMS-evoked activity was predicted by the modularity and structural integrity of the stimulated network rather than the targeted region(s) or the whole-brain connectivity, suggesting network-level structural connectivity as more relevant than local and global brain properties in shaping TMS signal propagation. The importance of network structural connectome was unveiled only by evoked activity, but not resting-state data. Future clinicals interventions might enhance target engagement by adopting DWI-guided, network-focused TMS.

Recent studies have revealed the possible role of choroid plexus (ChP) in Alzheimer's disease (AD). T1-weighted MRI is the modality of choice for the segmentation of ChP in humans. Manual segmentation is considered the gold-standard technique, but given its time-consuming nature, large-scale neuroimaging studies of ChP would be impossible. In this study, we introduce a lightweight segmentation algorithm based on the Gaussian Mixture Model (GMM). We compared its performance against manual segmentation as well as automated segmentation by Freesurfer in three separate datasets: 1) patients with structural MRIs enhanced with contrast (n = 19), 2) young healthy subjects (n = 20), and 3) patients with AD (n = 20). GMM outperformed Freesurfer and showed high similarity with manual segmentation. To further assess the algorithm's performance in large scale studies, we performed GMM segmentations in young healthy subjects from the Human Connectome Project (n = 1,067), as well as healthy controls, mild cognitive impairment (MCI), and AD patients from the Alzheimer's Disease Neuroimaging Initiative (n = 509). In both datasets, GMM segmented ChP more accurately than Freesurfer. To show the clinical importance of accurate ChP segmentation, total AV1451 (tau) PET binding to ChP was measured in 108 MCI and 32 AD patients. GMM was able to reveal the higher AV1451 binding to ChP in AD compared with MCI. Our results provide evidence for the utility of the GMM in accurately segmenting ChP and show its clinical relevance in AD. Future structural and functional studies of ChP will benefit from GMM's accurate segmentation.

BACKGROUND AND PURPOSE: The Bayesian probabilistic method has shown promising results to offset noise-related variability in perfusion analysis. Using CTP, we aimed to find optimal Bayesian-estimated thresholds based on multiparametric voxel-level models to estimate the ischemic core in patients with acute ischemic stroke. MATERIALS AND METHODS: Patients with anterior circulation acute ischemic stroke who had baseline CTP and achieved successful recanalization were included. In a subset of patients, multiparametric voxel-based models were constructed between Bayesian-processed CTP maps and follow-up MRIs to identify pretreatment CTP parameters that were predictive of infarction using robust logistic regression. Subsequently CTP-estimated ischemic core volumes from our Bayesian model were compared against routine clinical practice oscillation singular value decomposition-relative cerebral blood flow <30%, and the volumetric accuracy was assessed against final infarct volume. RESULTS: In the constructed multivariate voxel-based model, 4 variables were identified as independent predictors of infarction: TTP, relative CBF, differential arterial tissue delay, and differential mean transit time. At an optimal cutoff point of 0.109, this model identified infarcted voxels with nearly 80% accuracy. The limits of agreement between CTP-estimated ischemic core and final infarct volume ranged from -25 to 27 mL for the Bayesian model, compared with -61 to 52 mL for oscillation singular value decomposition-relative CBF. CONCLUSIONS: We established thresholds for the Bayesian model to estimate the ischemic core. The described multiparametric Bayesian-based model improved consistency in CTP estimation of the ischemic core compared with the methodology used in current clinical routine.