Degree Of Functional Connectivity Research Articles

Integrating Habitat Suitability and Larval Drift Modeling for Spawning‐To‐Nursery Functional Habitat Connectivity Analysis in Rivers

AbstractHabitat suitability modeling is a commonly used methodology to plan and assess in‐stream habitat enhancement in rivers, such as for the key fish life stages spawning and juvenile development. However, their use only allows modeling the spatial distribution of habitats, but not their connectivity. By integrating micro‐scale habitat modeling and a larval drift model, we assess the functional connectivity between spawning and larval nursery habitats for four rheophilic and litophilic fish species in a channelized and hydropower impacted reach of the lower Inn River (Bavaria, Germany), in which two restoration measures, a bypass channel and an island side‐channel system, have been constructed to improve longitudinal connectivity and habitat conditions. The study aims to (a) map spawning and larval nursery habitats, (b) quantify their connectivity, and (c) optimize functional habitat connectivity through an alternative bypass channel location. Results show that the channel's morphological complexity influences quantity and quality of available spawning and nursery habitats and their connectivity. Despite the presence of nursery habitats across the analyzed channel, the slow lateral larval dispersion during drift limits their accessibility to only the left river bank, making only 33% of available habitats useable. The degree of functional connectivity and hence the percentage of useable habitats can however be increased up to 95.3 % when considering different spatial configurations of habitats, which were explored in two alternative restoration scenarios. The results demonstrate the importance of considering functional habitat connectivity in habitat assessments and restoration planning.

Causal evidence of a line attractor encoding an affective state

Continuous attractors are an emergent property of neural population dynamics that have been hypothesized to encode continuous variables such as head direction and eye position1–4. In mammals, direct evidence of neural implementation of a continuous attractor has been hindered by the challenge of targeting perturbations to specific neurons within contributing ensembles2,3. Dynamical systems modelling has revealed that neurons in the hypothalamus exhibit approximate line-attractor dynamics in male mice during aggressive encounters5. We have previously hypothesized that these dynamics may encode the variable intensity and persistence of an aggressive internal state. Here we report that these neurons also showed line-attractor dynamics in head-fixed mice observing aggression6. This allowed us to identify and manipulate line-attractor-contributing neurons using two-photon calcium imaging and holographic optogenetic perturbations. On-manifold perturbations yielded integration of optogenetic stimulation pulses and persistent activity that drove the system along the line attractor, while transient off-manifold perturbations were followed by rapid relaxation back into the attractor. Furthermore, single-cell stimulation and imaging revealed selective functional connectivity among attractor-contributing neurons. Notably, individual differences among mice in line-attractor stability were correlated with the degree of functional connectivity among attractor-contributing neurons. Mechanistic recurrent neural network modelling indicated that dense subnetwork connectivity and slow neurotransmission7 best recapitulate our empirical findings. Our work bridges circuit and manifold levels3, providing causal evidence of continuous attractor dynamics encoding an affective internal state in the mammalian hypothalamus.

Intrinsic Dynamics and Neural Implementation of a Hypothalamic Line Attractor Encoding an Internal Behavioral State.

Line attractors are emergent population dynamics hypothesized to encode continuous variables such as head direction and internal states. In mammals, direct evidence of neural implementation of a line attractor has been hindered by the challenge of targeting perturbations to specific neurons within contributing ensembles. Estrogen receptor type 1 (Esr1)-expressing neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) show line attractor dynamics in male mice during fighting. We hypothesized that these dynamics may encode continuous variation in the intensity of an internal aggressive state. Here, we report that these neurons also show line attractor dynamics in head-fixed mice observing aggression. We exploit this finding to identify and perturb line attractor-contributing neurons using 2-photon calcium imaging and holographic optogenetic perturbations. On-manifold perturbations demonstrate that integration and persistent activity are intrinsic properties of these neurons which drive the system along the line attractor, while transient off-manifold perturbations reveal rapid relaxation back into the attractor. Furthermore, stimulation and imaging reveal selective functional connectivity among attractor-contributing neurons. Intriguingly, individual differences among mice in line attractor stability were correlated with the degree of functional connectivity among contributing neurons. Mechanistic modelling indicates that dense subnetwork connectivity and slow neurotransmission are required to explain our empirical findings. Our work bridges circuit and manifold paradigms, shedding light on the intrinsic and operational dynamics of a behaviorally relevant mammalian line attractor.

Spatiotemporal Dynamics of Successive Activations across the Human Brain during Simple Arithmetic Processing.

Previous neuroimaging studies have offered unique insights about the spatial organization of activations and deactivations across the brain; however, these were not powered to explore the exact timing of events at the subsecond scale combined with a precise anatomical source of information at the level of individual brains. As a result, we know little about the order of engagement across different brain regions during a given cognitive task. Using experimental arithmetic tasks as a prototype for human-unique symbolic processing, we recorded directly across 10,076 brain sites in 85 human subjects (52% female) using the intracranial electroencephalography. Our data revealed a remarkably distributed change of activity in almost half of the sampled sites. In each activated brain region, we found juxtaposed neuronal populations preferentially responsive to either the target or control conditions, arranged in an anatomically orderly manner. Notably, an orderly successive activation of a set of brain regions-anatomically consistent across subjects-was observed in individual brains. The temporal order of activations across these sites was replicable across subjects and trials. Moreover, the degree of functional connectivity between the sites decreased as a function of temporal distance between regions, suggesting that the information is partially leaked or transformed along the processing chain. Our study complements prior imaging studies by providing hitherto unknown information about the timing of events in the brain during arithmetic processing. Such findings can be a basis for developing mechanistic computational models of human-specific cognitive symbolic systems.

Brain age monotonicity and functional connectivity differences of healthy subjects.

Alterations in the brain's connectivity or the interactions among brain regions have been studied with the aid of resting state (rs)fMRI data attained from large numbers of healthy subjects of various demographics. This has been instrumental in providing insight into how a phenotype as fundamental as age affects the brain. Although machine learning (ML) techniques have already been deployed in such studies, novel questions are investigated in this work. We study whether young brains develop properties that progressively resemble those of aged brains, and if the aging dynamics of older brains provide information about the aging trajectory in young subjects. The degree of a prospective monotonic relationship will be quantified, and hypotheses of brain aging trajectories will be tested via ML. Furthermore, the degree of functional connectivity across the age spectrum of three datasets will be compared at a population level and across sexes. The findings scrutinize similarities and differences among the male and female subjects at greater detail than previously performed.

Functional reorganization of intranetwork and internetwork connectivity in patients with Ménière’s disease

Ménière’s disease (MD) is associated with functional reorganization not only in the auditory or sensory cortex but also in other control and cognitive areas. In this study, we examined intranetwork and internetwork connectivity differences between 55 MD patients and 70 healthy controls (HC) in 9 well-defined resting-state networks. Functional connectivity degree was lower in MD compared to HC in 19 brain areas involved in the somatomotor, auditory, ventral attention, default mode, limbic, and deep gray matter networks. In addition, we observed lower intranetwork connectivity in the auditory, ventral attention, and limbic networks, as well as lower internetwork connectivity between the somatomotor and limbic networks, and between the auditory and somatomotor, deep gray matter, and ventral attention networks, and between the deep gray matter and default mode network. Furthermore, we identified 81 pairs of brain areas with significant differences in functional connectivity between MD patients and HC at the edge level. Notably, the left amygdala’s functional connectivity degree was positively correlated with MD’s disease stage, and the ventral attention network’s intranetwork connectivity was positively correlated with the healthy side vestibular ratio. Our findings suggest that these functional network reorganization alterations may serve as potential biomarkers for predicting clinical progression, evaluating disease severity, and gaining a better understanding of MD’s pathophysiology. Large-scale network studies using neuroimaging techniques can provide additional insights into the underlying mechanisms of MD.

Functional connectomic profile correlates with effective anterior thalamic stimulation for refractory epilepsy

BackgroundDeep brain stimulation of the anterior nucleus of the thalamus (ANT-DBS) is an effective treatment for refractory epilepsy; however, seizure outcome varies among individuals. Identifying a reliable noninvasive biomarker to predict good responders would be helpful. ObjectivesTo test whether the functional connectivity between the ANT-DBS sites and the seizure foci correlates with effective seizure control in refractory epilepsy. MethodsWe performed a proof-of-concept pilot study of patients with focal refractory epilepsy receiving ANT-DBS. Using normative human connectome data derived from 1000 healthy participants, we investigated whether intrinsic functional connectivity between the seizure foci and the DBS site was associated with seizure outcome. We repeated this analysis controlling for the extent of seizure foci, distance between the seizure foci and DBS site, and using functional connectivity of the ANT instead of the DBS site to test the contribution of variance in DBS sites. ResultsEighteen patients with two or more seizure foci were included. Greater functional connectivity between the seizure foci and the DBS site correlated with more favorable outcome. The degree of functional connectivity accounted for significant variance in clinical outcomes (DBS site: |r| = 0.773, p < 0.001 vs ANT-atlas: |r| = 0.715, p = 0.001), which remained significant when controlling for the extent of the seizure foci (|r| = 0.773, p < 0.001) and the distance between the seizure foci and DBS site (|r| = 0.777, p < 0.001). Significant correlations were independent of variance in the DBS sites (|r| = 0.148, p = 0.57). ConclusionThese findings suggest that functional connectomic profile is a potential reliable non-invasive biomarker to predict ANT-DBS outcomes. Accordingly, the identification of ANT responders could decrease the surgical risk for patients who may not benefit and optimize the cost-effective allocation of health care resources.

Convergent and divergent intra- and internetwork connectivity in Parkinson's disease with wearing-off.

Our study aimed to explore the functional connectivity alterations between cortical nodes of resting-state networks in Parkinson's disease (PD) patients with wearing-off (WO) at different levels. Resting-state functional magnetic resonance imaging was performed on 36 PD patients without wearing-off (PD-nWO), 30 PD patients with wearing-off (PD-WO), and 35 healthy controls (HCs) to extract functional networks. Integrity, network, and edge levels were calculated for comparison between groups. UPDRS-III, MMSE, MOCA, HAMA, and HAMD scores were collected for further regression analysis. We observed significantly reduced connectivity strength in the dorsal attention network and limbic network in the PD-WO group compared with the HC group. The PD-WO group showed a decreased degree of functional connectivity at 12 nodes, including the bilateral orbital part of the superior frontal gyrus, right olfactory cortex, left medial orbital part of the superior frontal gyrus, bilateral gyrus rectus, right parahippocampal gyrus, right thalamus, left Heschl's gyrus, right superior temporal gyrus part of the temporal pole, left middle temporal gyrus part of the temporal pole, and right inferior temporal gyrus. Furthermore, the PD-WO group showed a significantly lower degree of functional connectivity in the left orbital part of the superior frontal gyrus and right gyrus rectus than the PD-nWO group. Internetwork analysis indicated reduced functional connectivity in five pairs of resting-state networks. Our results demonstrated altered intra- and internetwork connections in PD patients with WO. These findings will facilitate a better understanding of the distinction between the network changes in PD pathophysiology.

Glioblastoma remodelling of human neural circuits decreases survival

Gliomas synaptically integrate into neural circuits1,2. Previous research has demonstrated bidirectional interactions between neurons and glioma cells, with neuronal activity driving glioma growth1–4 and gliomas increasing neuronal excitability2,5–8. Here we sought to determine how glioma-induced neuronal changes influence neural circuits underlying cognition and whether these interactions influence patient survival. Using intracranial brain recordings during lexical retrieval language tasks in awake humans together with site-specific tumour tissue biopsies and cell biology experiments, we find that gliomas remodel functional neural circuitry such that task-relevant neural responses activate tumour-infiltrated cortex well beyond the cortical regions that are normally recruited in the healthy brain. Site-directed biopsies from regions within the tumour that exhibit high functional connectivity between the tumour and the rest of the brain are enriched for a glioblastoma subpopulation that exhibits a distinct synaptogenic and neuronotrophic phenotype. Tumour cells from functionally connected regions secrete the synaptogenic factor thrombospondin-1, which contributes to the differential neuron–glioma interactions observed in functionally connected tumour regions compared with tumour regions with less functional connectivity. Pharmacological inhibition of thrombospondin-1 using the FDA-approved drug gabapentin decreases glioblastoma proliferation. The degree of functional connectivity between glioblastoma and the normal brain negatively affects both patient survival and performance in language tasks. These data demonstrate that high-grade gliomas functionally remodel neural circuits in the human brain, which both promotes tumour progression and impairs cognition.

Functional orderly topography of brain networks associated with gene expression heterogeneity

The human cerebral cortex is vastly expanded relative to nonhuman primates and rodents, leading to a functional orderly topography of brain networks. Here, we show that functional topography may be associated with gene expression heterogeneity. The neocortex exhibits greater heterogeneity in gene expression, with a lower expression of housekeeping genes, a longer mean path length, fewer clusters, and a lower degree of ordering in networks than archicortical and subcortical areas in human, rhesus macaque, and mouse brains. In particular, the cerebellar cortex displays greater heterogeneity in gene expression than cerebellar deep nuclei in the human brain, but not in the mouse brain, corresponding to the emergence of novel functions in the human cerebellar cortex. Moreover, the cortical areas with greater heterogeneity, primarily located in the multimodal association cortex, tend to express genes with higher evolutionary rates and exhibit a higher degree of functional connectivity measured by resting-state fMRI, implying that such a spatial distribution of gene expression may be shaped by evolution and is favourable for the specialization of higher cognitive functions. Together, the cross-species imaging and genetic findings may provide convergent evidence to support the association between the orderly topography of brain function networks and gene expression.

Do oscillations in pancreatic islets require pacemaker cells?

The pancreatic islets of Langerhans are biomedically important because they are home to the beta cells that secrete insulin and are hence important for understanding diabetes. They are also an important case study for the mechanisms of bursting oscillations and how these oscillations emerge from the electrical coupling of highly heterogeneous cells. Early work has pointed to a voting/democratic paradigm, where the islet properties are a nonlinear average of the cell properties, with no 'conductor leading the orchestra'. Recent experimental work has uncovered new facets of this heterogeneity, and has identified small world networks dominated by a small subset of cells with a high degree of functional connectivity, assessed via correlations of calcium oscillations. It has also been suggested that these connectivity hubs act as pacemakers necessary for islet oscillations. We reviewed modeling studies that have confirmed the existence of small worldness, and we did not find evidence for obligatory pacemakers. We conclude that democracy rather than oligarchy remains the most likely organizing principle of the islets.

Common functional connectivity alterations in focal epilepsies identified by machine learning.

This study was undertaken to identify shared functional network characteristics among focal epilepsies of different etiologies, to distinguish epilepsy patients from controls, and to lateralize seizure focus using functional connectivity (FC) measures derived from resting state functional magnetic resonance imaging (MRI). Data were taken from 103 adult and 65 pediatric focal epilepsy patients (with or without lesion on MRI) and 109 controls across four epilepsy centers. We used three whole-brain FC measures: parcelwise connectivity matrix, mean FC, and degree of FC. We trained support vector machine models with fivefold cross-validation (1) to distinguish patients from controls and (2) to lateralize the hemisphere of seizure onset in patients. We reported the regions and connections with the highest importance from each model as the common FC differences between the compared groups. FC measures related to the default mode and limbic networks had higher importance relative to other networks for distinguishing epilepsy patients from controls. In lateralization models, regions related to somatosensory, visual, default mode, and basal ganglia showed higher importance. The epilepsy versus control classification model trained using a 400-parcel connectivity matrix achieved a median testing accuracy of 75.6% (median area under the curve [AUC]=.83) in repeated independent testing. Lateralization accuracy using the 400-parcel connectivity matrix reached a median accuracy of 64.0% (median AUC=.69). Machine learning models revealed common FC alterations in a heterogeneous group of patients with focal epilepsies. The distribution of the most altered regions supports the hypothesis that shared functional alteration exists beyond the seizure onset zone and its epileptic network. We showed that FC measures can distinguish patients from controls, and further lateralize focal epilepsies. Future studies are needed to confirm these findings by using larger numbers of epilepsy patients.

Gait Variability Is Associated With the Strength of Functional Connectivity Between the Default and Dorsal Attention Brain Networks: Evidence From Multiple Cohorts.

In older adults, elevated gait variability when walking has been associated with both cognitive impairment and future falls. This study leveraged 3 existing data sets to determine relationships between gait variability and the strength of functional connectivity within and between large-scale brain networks in healthy older adults, those with mild-to-moderate functional impairment, and those with Parkinson's disease (PD). Gait and resting-state functional magnetic resonance imaging data were extracted from existing data sets on: (i) 12 older adults without overt disease yet with slow gait and mild executive dysfunction; (ii) 12 older adults with intact cognitive-motor function and age- and sex-matched to the first cohort; and (iii) 15 individuals with PD. Gait variability (%, coefficient of variation of stride time) during preferred walking speed was measured and correlated with the degree of functional connectivity within and between 7 established large-scale functional brain networks. Regression models adjusted for age and sex revealed that in each cohort, those with less gait variability exhibited greater negative correlation between fluctuations in resting-state brain activity between the default network and the dorsal attention network (functionally limited older: β = 4.38, p = .027; healthy older: β = 1.66, p = .032; PD: β = 1.65, p = .005). No other within- or between-network connectivity outcomes were consistently related to gait variability across all 3 cohorts. These results provide strong evidence that gait variability is uniquely related to functional connectivity between the default network and the dorsal attention network, and that this relationship may be independent of both functional status and underlying brain disease.

Brain parcellation driven by dynamic functional connectivity better capture intrinsic network dynamics.

Until now, dynamic functional connectivity (dFC) based on functional magnetic resonance imaging is typically estimated on a set of predefined regions of interest (ROIs) derived from an anatomical or static functional atlas which follows an implicit assumption of functional homogeneity within ROIs underlying temporal fluctuation of functional coupling, potentially leading to biases or underestimation of brain network dynamics. Here, we presented a novel computational method based on dynamic functional connectivity degree (dFCD) to derive meaningful brain parcellations that can capture functional homogeneous regions in temporal variance of functional connectivity. Several spatially distributed but functionally meaningful areas that are well consistent with known intrinsic connectivity networks were identified through independent component analysis (ICA) of time‐varying dFCD maps. Furthermore, a systematical comparison with commonly used brain atlases, including the Anatomical Automatic Labeling template, static ICA‐driven parcellation and random parcellation, demonstrated that the ROI‐definition strategy based on the proposed dFC‐driven parcellation could better capture the interindividual variability in dFC and predict observed individual cognitive performance (e.g., fluid intelligence, cognitive flexibility, and sustained attention) based on chronnectome. Together, our findings shed new light on the functional organization of resting brains at the timescale of seconds and emphasized the significance of a dFC‐driven and voxel‐wise functional homogeneous parcellation for network dynamics analyses in neuroscience.

3-D ocean particle tracking modeling reveals extensive vertical movement and downstream interdependence of closed areas in the northwest Atlantic

Novel 3-D passive particle tracking experiments were performed in the northwest Atlantic to elucidate connectivity among areas closed to protect vulnerable marine ecosystems. We examined (1) the degree of vertical movement of particles released at different depths and locations; (2) the location of potential source populations for the deep-sea taxa protected by the closures; and (3) the degree of functional connectivity. A long-term oceanographic dataset (EN4) was queried to characterize the temperature and salinity regimes in each of the closed areas as a basis for interpreting recently published climate change projections. Using the Parcels Lagrangian particle tracking framework and the BNAM hydrodynamic model, we found enhanced connectivity over previously developed 2-D models and unexpected, current-driven, strong (to a maximum of about 1340 m) downward displacement at depth (450, 1000 and 2250 m), with weaker upward displacement except for the release depth of 2250 m which showed upward movement of 955 m with a drift duration of 3 months. The current velocities create down-stream interdependence among closed areas and allow redundancy to develop in some of the areas of the network, with some of the larger areas also showing retention. Source populations for sponges in the upstream closure are likely in adjacent waters of the Canadian continental shelf. Collectively this information can be used to inform management decisions related to the size and placement of these closed areas, and vertical velocity surfaces have potential for use in species distribution modeling of benthic species and habitats.

Identifying the functional brain network of motor reserve in early Parkinson's disease

Objective: The severity of motor symptoms in Parkinson's disease (PD) does not always correlate with the degree of nigral dopaminergic neuronal loss. Individuals with greater motor reserve may have milder motor signs than their striatal dopamine loss. In this study, we explored the functional brain network associated with motor reserve in early-stage PD.

Brain Functional Connectivity Correlates of Subclinical Obsessive-Compulsive Symptoms in Healthy Children

Commonly observed subclinical obsessive-compulsive symptoms in healthy children may predispose to obsessive-compulsive disorder (OCD). Therefore, investigating the underlying neurobiology may be relevant to identify alterations in specific brain circuits potentially accounting for clinical heterogeneity in OCD without the confounding effects of clinical samples. We analyzed the brain correlates of different obsessive-compulsive symptoms in a large group of healthy children using functional connectivity measures. We evaluated 227 healthy children (52% girls; mean [SD] age 9.71 [0.86] years; range, 8-12.1 years). Participants underwent clinical assessment with the Obsessive-Compulsive Inventory-Child Version and a resting-state functional magnetic resonance imaging examination. Total and symptom-specific severity were correlated with voxelwise global functional connectivity degree values. Significant clusters were then used as seeds of interest in seed-to-voxel analyses. Modulating effects of age and sex were also assessed. Global functional connectivity of the left ventral putamen and medial dorsal thalamus correlated negatively with total obsessive-compulsive symptom severity. Seed-to-voxel analyses revealed specific negative correlations from these clusters with limbic, sensorimotor, and insular regions in association with obsessing, ordering, and doubt-checking symptoms, respectively. Hoarding symptoms were associated with negative correlations between the left medial dorsal thalamus and a widespread pattern of regions, with such associations modulated by sex and age. Our findings concur with prevailing neurobiological models of OCD on the importance of cortico-striato-thalamo-cortical dysfunction to account for symptom severity. Notably, we showed that changes in cortico-striato-thalamo-cortical connectivity are present at subclinical stages, which may result in an increased vulnerability for OCD. Moreover, we mapped different symptom dimensions onto specific cortico-striato-thalamo-cortical circuit attributes.

Organ System Network Disruption Is Associated With Poor Prognosis in Patients With Chronic Liver Failure.

BackgroundA healthy individual has a high degree of functional connectivity between organ systems, which can be represented graphically in a network map. Disruption of this system connectivity is associated with mortality in life-threatening acute illnesses, demonstrated by a network approach. However, this approach has not been applied to chronic multisystem diseases and may be more reliable than conventional individual organ prognostic scoring methods. Cirrhosis is a chronic disease of the liver with multisystem involvement. Development of an efficient model for prediction of mortality in cirrhosis requires a profound understanding of the pathophysiologic processes that lead to poor prognosis. In the present study, we use a network approach to evaluate the differences in organ system connectivity between survivors and non-survivors in a group of well-characterized patients with cirrhosis.Methods201 patients with cirrhosis originally referred to the Clinic five at the University Hospital of Padova, with 13 clinical variables available representing hepatic, metabolic, haematopoietic, immune, neural and renal organ systems were retrospectively enrolled and followed up for 3, 6, and 12 months. Software was designed to compute the correlation network maps of organ system interaction in survivors and non-survivors using analysis indices: A. Bonferroni corrected Pearson’s correlation coefficient and B. Mutual Information. Network structure was quantitatively evaluated using the measures of edges, average degree of connectivity and closeness, and qualitatively using clinical significance. Pair-matching was also implemented as a further step after initial general analysis to control for sample size and Model for End-Stage Liver Disease (MELD-Na) score between the groups.ResultsThere was a higher number of significant correlations in survivors, as indicated by both the analysis indices of Bonferroni corrected Pearson’s correlation coefficient and the Mutual Information analysis. The number of edges, average degree of connectivity and closeness were significantly higher in survivors compared to non-survivors group. Pair-matching for MELD-Na was also associated with increased connectivity in survivors compared to non-survivors over 3 and 6 months follow up.ConclusionThis study demonstrates the application of a network approach in evaluating functional connectivity of organ systems in liver cirrhosis, demonstrating a significant degree of network disruption in organ systems in non-survivors. Network analysis of organ systems may provide insight in developing novel prognostic models for organ allocation in patients with cirrhosis.

4488 Neural Network of the Cognitive Model of Reading

OBJECTIVES/GOALS: A particularly debilitating consequence of stroke is alexia, an acquired impairment in reading. Cognitive models aim to characterize how information is processed based on behavioral data. If we can concurrently characterize how neural networks process that information, we can enhance the models to reflect the neuronal interactions that drive them. METHODS/STUDY POPULATION: There will be 10 unimpaired adult readers. Two functional localizer tasks, deigned to consistently activate robust language areas, identify the regions of interest that process the cognitive reading functions (orthography, phonology, semantics). Another task, designed for this experiment, analyses the reading-related functional-connectivity between these areas by presenting words classified along the attributes of frequency, concreteness, and regularity, which utilize specific cognitive routes, and a visual control. Connectivity is analyzed during word reading overall vs. a control condition to determine overall reading-related connectivity, and while reading words that have high vs. low attribute values, to determine if cognitive processing routes bias the neural reading network connectivity. RESULTS/ANTICIPATED RESULTS: The localizer analysis is expected to result in the activation of canonical reading areas. The degree of functional connectivity observed between these regions is expected to depend on the degree to which each cognitive route is utilized to read a given word. After orthographic, phonologic, and semantic areas have been identified, the connectivity analysis should show that there is high correlation between all three types of areas during reading compared to the control condition. Then the frequency, regularity, and concreteness of the words being read should alter the reliance on the pathways between these area types. This would support the hypothesized pattern of connectivity as predicted by the cognitive reading routes. Otherwise, it will show how the neural reading network differs from the cognitive model. DISCUSSION/SIGNIFICANCE OF IMPACT: The results will determine the relationship between the cognitive reading model and the neural reading network. Cognitive models show what processes occur in the brain, but neural networks show how these processes occur. By relating these components, we obtain a more complete view of reading in the brain, which can inform future alexia treatments.

Identifying the Functional Brain Network of Motor Reserve in Early Parkinson's Disease.

The severity of motor symptoms in Parkinson's disease (PD) does not always correlate with the degree of nigral dopaminergic neuronal loss. Individuals with greater motor reserve may have milder motor signs than their striatal dopamine loss. In this study, we explored the functional brain network associated with motor reserve in early-stage PD. We analyzed 134 patients with de novo PD who underwent dopamine transporter scans and resting-state functional magnetic resonance imaging. We estimated individual motor reserve based on initial motor deficits and striatal dopamine depletion using a residual model. We applied network-based statistic analysis to identify the functional brain network associated with the measure of motor reserve (ie, motor reserve network). We also assessed the effect of motor reserve network connectivity strength on the longitudinal increase in levodopa-equivalent dose during the 2-year follow-up period. Network-based statistic analysis identified the motor reserve network composed of the basal ganglia, inferior frontal cortex, insula, and cerebellar vermis at a primary threshold of P value 0.001. Patients with an increased degree of functional connectivity within the motor reserve network had greater motor reserve. There was a significant interaction between the motor reserve network strength and time in the linear mixed model, indicating that higher motor reserve network strength was associated with slower longitudinal increase in levodopa-equivalent dose. The present study revealed the functional brain network associated with motor reserve in patients with early-stage PD. Functional connections within the motor reserve network are associated with the individual's capacity to cope with PD-related pathologies. © 2020 International Parkinson and Movement Disorder Society.