Correlations between developmental trajectories of brain functional connectivity, neurocognitive functions, and clinical symptoms in patients with attention-deficit hyperactivity disorder.

Several studies on attention-deficit hyperactivity disorder (ADHD) have suggested a developmental sequence of brain changes: subcortico-subcortical connectivity in children, evolving to subcortico-cortical in adolescence, and culminating in cortico-cortical connectivity in young adulthood. This study hypothesized that children with ADHD would exhibit decreased functional connectivity (FC) between the cortex and striatum compared to adults with ADHD, who may show increased FC in these regions. Seventy-six patients with ADHD (26 children, 26 adolescents, and 24 adults) and 74 healthy controls (25 children, 24 adolescents, and 25 adults) participated in the study. Resting state magnetic resonance images were acquired using a 3.0 T Philips Achieva scanner. The results indicated a gradual decrease in the number of subcategories representing intelligence quotient deficits in the ADHD group with age. In adulthood, the ADHD group exhibited lower working memory compared to the healthy control group. The number of regions showing decreased FC from the cortex to striatum between the ADHD and control groups reduced with age, while regions with increased FC from the default mode network and attention network in the ADHD group increased with age. In adolescents and adults, working memory was positively associated with brain activity in the postcentral gyrus and negatively correlated with ADHD clinical symptoms. In conclusion, the findings suggest that intelligence deficits in certain IQ subcategories may diminish as individuals with ADHD age. Additionally, the study indicates an increasing anticorrelation between cortical and subcortical regions with age in individuals with ADHD.

From compulsivity to compulsion: the neural basis of compulsive disorders.

Compulsive behaviour, an apparently irrational perseveration in often maladaptive acts, is a potential transdiagnostic symptom of several neuropsychiatric disorders, including obsessive-compulsive disorder and addiction, and may reflect the severe manifestation of a dimensional trait termed compulsivity. In this Review, we examine the psychological basis of compulsions and compulsivity and their underlying neural circuitry using evidence from human neuroimaging and animal models. Several main elements of this circuitry are identified, focused on fronto-striatal systems implicated in goal-directed behaviour and habits. These systems include the orbitofrontal, prefrontal, anterior cingulate and insular cortices and their connections with the basal ganglia as well as sensoriomotor and parietal cortices and cerebellum. We also consider the implications for future classification of impulsive-compulsive disorders and their treatment.

Effect of Stress-Related Neural Pathways on the Cardiovascular Benefit of Physical Activity.

BACKGROUND: The mechanisms underlying the psychological and cardiovascular disease (CVD) benefits of physical activity (PA) are not fully understood. OBJECTIVES: This study tested whether PA: 1) attenuates stress-related neural activity, which is known to potentiate CVD and for its role in anxiety/depression; 2) decreases CVD in part through this neural effect; and 3) has a greater impact on CVD risk among individuals with depression. METHODS: Participants from the Mass General Brigham Biobank who completed a PA survey were studied. A subset underwent 18F-fluorodeoxyglucose positron emission tomography/computed tomographic imaging. Stress-related neural activity was measured as the ratio of resting amygdalar-to-cortical activity (AmygAC). CVD events were ascertained from electronic health records. RESULTS: A total of 50,359 adults were included (median age 60 years [Q1-Q3: 45-70 years]; 40.1% male). Greater PA was associated with both lower AmygAC (standardized ß: -0.245; 95% CI: -0.444 to -0.046; P = 0.016) and CVD events (HR: 0.802; 95% CI: 0.719-0.896; P < 0.001) in multivariable models. AmygAC reductions partially mediated PA's CVD benefit (OR: 0.96; 95% CI: 0.92-0.99; P < 0.05). Moreover, PA's benefit on incident CVD events was greater among those with (vs without) preexisting depression (HR: 0.860; 95% CI: 0.810-0.915; vs HR: 0.929; 95% CI: 0.910-0.949; P interaction = 0.011). Additionally, PA above guideline recommendations further reduced CVD events, but only among those with preexisting depression (P interaction = 0.023). CONCLUSIONS: PA appears to reduce CVD risk in part by acting through the brain's stress-related activity; this may explain the novel observation that PA reduces CVD risk to a greater extent among individuals with depression.

Brain activation and connection across resting and motor-task states in patients with generalized tonic-clonic seizures.

AIMS: Motor abnormalities have been identified as one common symptom in patients with generalized tonic-clonic seizures (GTCS) inspiring us to explore the disease in a motor execution condition, which might provide novel insight into the pathomechanism. METHODS: Resting-state and motor-task fMRI data were collected from 50 patients with GTCS, including 18 patients newly diagnosed without antiepileptic drugs (ND_GTCS) and 32 patients receiving antiepileptic drugs (AEDs_GTCS). Motor activation and its association with head motion and cerebral gradients were assessed. Whole-brain network connectivity across resting and motor states was further calculated and compared between groups. RESULTS: All patients showed over-activation in the postcentral gyrus and the ND_GTCS showed decreased activation in putamen. Specifically, activation maps of ND_GTCS showed an abnormal correlation with head motion and cerebral gradient. Moreover, we detected altered functional network connectivity in patients within states and across resting and motor states by using repeated-measures analysis of variance. Patients did not show abnormal connectivity in the resting state, while distributed abnormal connectivity in the motor-task state. Decreased across-state network connectivity was also found in all patients. CONCLUSION: Convergent findings suggested the over-response of activation and connection of the brain to motor execution in GTCS, providing new clues to uncover motor susceptibility underlying the disease.

Brain areas interconnected to ventral pathway circuits are independently able to induce enhancement in object recognition memory and cause reversal in object recognition memory deficit.

AIMS: Ventral pathway circuits are constituted by the interconnected brain areas that are distributed throughout the brain. These brain circuits are primarily involved in processing of object related information in brain. However, their role in object recognition memory (ORM) enhancement remains unknown. Here, we have studied on the implication of these circuits in ORM enhancement and in reversal of ORM deficit in aging. METHODS: The brain areas interconnected to ventral pathway circuits in rat brain were activated by an expression of a protein called regulator of G-protein signaling 14 of 414 amino acids (RGS14414). RGS14414 is an ORM enhancer and therefore used here as a gain-in-function tool. ORM test and immunohistochemistry, lesions, neuronal arborization, and knockdown studies were performed to uncover the novel function of ventral pathway circuits. RESULTS: An activation of each of the brain areas interconnected to ventral pathway circuits individually induced enhancement in ORM; however, same treatment in brain areas not interconnected to ventral pathway circuits produced no effect. Further study in perirhinal cortex (PRh), area V2 of visual cortex and frontal cortex (FrC), which are brain areas that have been shown to be involved in ORM and are interconnected to ventral pathway circuits, revealed that ORM enhancement seen after the activation of any one of the three brain areas was unaffected by the lesions in other two brain areas either individually in each area or even concurrently in both areas. This ORM enhancement in all three brain areas was associated to increase in structural plasticity of pyramidal neurons where more than 2-fold higher dendritic spines were observed. Additionally, we found that an activation of either PRh, area V2, or FrC not only was adequate but also was sufficient for the reversal of ORM deficit in aging rats, and the blockade of RGS14414 activity led to loss in increase in dendritic spine density and failure in reversal of ORM deficit. CONCLUSIONS: These results suggest that brain areas interconnected to ventral pathway circuits facilitate ORM enhancement by an increase in synaptic connectivity between the local brain area circuits and the passing by ventral pathway circuits and an upregulation in activity of ventral pathway circuits. In addition, the finding of the reversal of ORM deficit through activation of an interconnected brain area might serve as a platform for developing not only therapy against memory deficits but also strategies for other brain diseases in which neuronal circuits are compromised.

Electrophysiological and morphological properties of prefrontal pyramidal neurons innervated by mediodorsal thalamus.

The high-order cognitive and executive functions are necessary for an individual to survive. The densely bidirectional innervations between the medial prefrontal cortex (mPFC) and the mediodorsal thalamus (MD) play a vital role in regulating high-order functions. Pyramidal neurons in mPFC have been classified into several subclasses according to their morphological and electrophysiological properties, but the properties of the input-specific pyramidal neurons in mPFC remain poorly understood. The present study aimed to profile the morphological and electrophysiological properties of mPFC pyramidal neurons innervated by MD. In the past, the studies for characterizing the morphological and electrophysiological properties of neurons mainly relied on the electrophysiological recording of a large number of neurons and their morphologic reconstructions. But, it is a low efficient method for characterizing the circuit-specific neurons. The present study combined the advantages of traditional morphological and electrophysiological methods with machine learning to address the shortcomings of the past method, to establish a classification model for the morphological and electrophysiological properties of mPFC pyramidal neurons, and to achieve more accurate and efficient identification of the properties from a small size sample of neurons. We labeled MD-innervated pyramidal neurons of mPFC using the trans-synaptic neural circuitry tracing method and obtained their morphological properties using whole-cell patch-clamp recording and morphologic reconstructions. The results showed that the classification model established in the present study could predict the electrophysiological properties of MD-innervated pyramidal neurons based on their morphology. MD-innervated pyramidal neurons exhibit larger basal dendritic length but lower apical dendrite complexity compared to non-MD-innervated neurons in the mPFC. The morphological characteristics of the two subtypes (ET-1 and ET-2) of mPFC pyramidal neurons innervated by MD are different, with the apical dendrites of ET-1 neurons being longer and more complex than those of ET-2 neurons. These results suggest that the electrophysiological properties of MD- innervated pyramidal neurons within mPFC correlate with their morphological properties, indicating that the different roles of these two subclasses in local circuits within PFC, as well as in PFC-cortical/subcortical brain region circuits.

Synergism between two BLA-to-BNST pathways for appropriate expression of anxiety-like behaviors in male mice.

Understanding how distinct functional circuits are coordinated to fine-tune mood and behavior is of fundamental importance. Here, we observe that within the dense projections from basolateral amygdala (BLA) to bed nucleus of stria terminalis (BNST), there are two functionally opposing pathways orchestrated to enable contextually appropriate expression of anxiety-like behaviors in male mice. Specifically, the anterior BLA neurons predominantly innervate the anterodorsal BNST (adBNST), while their posterior counterparts send massive fibers to oval BNST (ovBNST) with moderate to adBNST. Optogenetic activation of the anterior and posterior BLA inputs oppositely regulated the activity of adBNST neurons and anxiety-like behaviors, via disengaging and engaging the inhibitory ovBNST-to-adBNST microcircuit, respectively. Importantly, the two pathways exhibited synchronized but opposite responses to both anxiolytic and anxiogenic stimuli, partially due to their mutual inhibition within BLA and the different inputs they receive. These findings reveal synergistic interactions between two BLA-to-BNST pathways for appropriate anxiety expression with ongoing environmental demands.

Parkinsonism originates in a discrete secondary and dystonia in a primary motor cortical-basal ganglia subcircuit.

Although manifesting contrasting phenotypes, Parkinson's disease and dystonia, the two most common movement disorders, can originate from similar pathophysiology. Previously, we demonstrated that lesioning (silencing) of a discrete dorsal region in the globus pallidus (rodent equivalent to globus pallidus externa) in rats and produced parkinsonism, while lesioning a nearby ventral hotspot-induced dystonia. Presently, we injected fluorescent-tagged multi-synaptic tracers into these pallidal hotspots (n = 36 Long Evans rats) and permitted 4 days for the viruses to travel along restricted connecting pathways and reach the motor cortex before sacrificing the animals. Viral injections in the Parkinson's hotspot fluorescent labeled a circumscribed region in the secondary motor cortex, while injections in the dystonia hotspot labeled within the primary motor cortex. Custom probability mapping and N200 staining affirmed the segregation of the cortical territories for Parkinsonism and dystonia to the secondary and primary motor cortices. Intracortical microstimulation localized territories specifically to their respective rostral and caudal microexcitable zones. Parkinsonian features are thus explained by pathological signaling within a secondary motor subcircuit normally responsible for initiation and scaling of movement, while dystonia is explained by abnormal (and excessive) basal ganglia signaling directed at primary motor corticospinal transmission.

The language network as a natural kind within the broader landscape of the human brain.

Language behaviour is complex, but neuroscientific evidence disentangles it into distinct components supported by dedicated brain areas or networks. In this Review, we describe the 'core' language network, which includes left-hemisphere frontal and temporal areas, and show that it is strongly interconnected, independent of input and output modalities, causally important for language and language-selective. We discuss evidence that this language network plausibly stores language knowledge and supports core linguistic computations related to accessing words and constructions from memory and combining them to interpret (decode) or generate (encode) linguistic messages. We emphasize that the language network works closely with, but is distinct from, both lower-level - perceptual and motor - mechanisms and higher-level systems of knowledge and reasoning. The perceptual and motor mechanisms process linguistic signals, but, in contrast to the language network, are sensitive only to these signals' surface properties, not their meanings; the systems of knowledge and reasoning (such as the system that supports social reasoning) are sometimes engaged during language use but are not language-selective. This Review lays a foundation both for in-depth investigations of these different components of the language processing pipeline and for probing inter-component interactions.

Salience network connectivity is altered in 6-week-old infants at heightened likelihood for developing autism.

Converging evidence implicates disrupted brain connectivity in autism spectrum disorder (ASD); however, the mechanisms linking altered connectivity early in development to the emergence of ASD symptomatology remain poorly understood. Here we examined whether atypicalities in the Salience Network - an early-emerging neural network involved in orienting attention to the most salient aspects of one's internal and external environment - may predict the development of ASD symptoms such as reduced social attention and atypical sensory processing. Six-week-old infants at high likelihood of developing ASD based on family history exhibited stronger Salience Network connectivity with sensorimotor regions; infants at typical likelihood of developing ASD demonstrated stronger Salience Network connectivity with prefrontal regions involved in social attention. Infants with higher connectivity with sensorimotor regions had lower connectivity with prefrontal regions, suggesting a direct tradeoff between attention to basic sensory versus socially-relevant information. Early alterations in Salience Network connectivity predicted subsequent ASD symptomatology, providing a plausible mechanistic account for the unfolding of atypical developmental trajectories associated with vulnerability to ASD.

Diverse and asymmetric patterns of single-neuron projectome in regulating interhemispheric connectivity.

The corpus callosum, historically considered primarily for homotopic connections, supports many heterotopic connections, indicating complex interhemispheric connectivity. Understanding this complexity is crucial yet challenging due to diverse cell-specific wiring patterns. Here, we utilized public AAV bulk tracing and single-neuron tracing data to delineate the anatomical connection patterns of mouse brains and conducted wide-field calcium imaging to assess functional connectivity across various brain states in male mice. The single-neuron data uncovered complex and dense interconnected patterns, particularly for interhemispheric-heterotopic connections. We proposed a metric "heterogeneity" to quantify the complexity of the connection patterns. Computational modeling of these patterns suggested that the heterogeneity of upstream projections impacted downstream homotopic functional connectivity. Furthermore, higher heterogeneity observed in interhemispheric-heterotopic projections would cause lower strength but higher stability in functional connectivity than their intrahemispheric counterparts. These findings were corroborated by our wide-field functional imaging data, underscoring the important role of heterotopic-projection heterogeneity in interhemispheric communication.

Whole-brain connections of glutamatergic neurons in the mouse lateral habenula in both sexes.

BACKGROUND: The lateral habenula (LHb) is an epithalamus nucleus that is evolutionarily conserved and involved in various physiological functions, such as encoding value signals, integrating emotional information, and regulating related behaviors. The cells in the LHb are predominantly glutamatergic and have heterogeneous functions in response to different stimuli. The circuitry connections of the LHb glutamatergic neurons play a crucial role in integrating a wide range of events. However, the circuitry connections of LHb glutamatergic neurons in both sexes have not been thoroughly investigated. METHODS: In this study, we injected Cre-dependent retrograde trace virus and anterograde synaptophysin-labeling virus into the LHb of adult male and female Vglut2-ires-Cre mice, respectively. We then quantitatively analyzed the input and output of the LHb glutamatergic connections in both the ipsilateral and contralateral whole brain. RESULTS: Our findings showed that the inputs to LHbvGlut2 neurons come from more than 30 brain subregions, including the cortex, striatum, pallidum, thalamus, hypothalamus, midbrain, pons, medulla, and cerebellum with no significant differences between males and females. The outputs of LHbvGlut2 neurons targeted eight large brain regions, primarily focusing on the midbrain and pons nuclei, with distinct features in presynaptic bouton across different brain subregions. While correlation and cluster analysis revealed differences in input and collateral projection features, the input-output connection pattern of LHbvGlut2 neurons in both sexes was highly similar. CONCLUSIONS: This study provides a systematic and comprehensive analysis of the input and output connections of LHbvGlut2 neurons in male and female mice, shedding light on the anatomical architecture of these specific cell types in the mouse LHb. This structural understanding can help guide further investigations into the complex functions of the LHb.

Modeling the neurocognitive dynamics of language across the lifespan.

Healthy aging is associated with a heterogeneous decline across cognitive functions, typically observed between language comprehension and language production (LP). Examining resting-state fMRI and neuropsychological data from 628 healthy adults (age 18-88) from the CamCAN cohort, we performed state-of-the-art graph theoretical analysis to uncover the neural mechanisms underlying this variability. At the cognitive level, our findings suggest that LP is not an isolated function but is modulated throughout the lifespan by the extent of inter-cognitive synergy between semantic and domain-general processes. At the cerebral level, we show that default mode network (DMN) suppression coupled with fronto-parietal network (FPN) integration is the way for the brain to compensate for the effects of dedifferentiation at a minimal cost, efficiently mitigating the age-related decline in LP. Relatedly, reduced DMN suppression in midlife could compromise the ability to manage the cost of FPN integration. This may prompt older adults to adopt a more cost-efficient compensatory strategy that maintains global homeostasis at the expense of LP performances. Taken together, we propose that midlife represents a critical neurocognitive juncture that signifies the onset of LP decline, as older adults gradually lose control over semantic representations. We summarize our findings in a novel synergistic, economical, nonlinear, emergent, cognitive aging model, integrating connectomic and cognitive dimensions within a complex system perspective.

Development of activity-based anorexia requires PKC-δ neurons in two central extended amygdala nuclei.

Anorexia nervosa (AN) is a serious psychiatric disease, but the neural mechanisms underlying its development are unclear. A subpopulation of amygdala neurons, marked by expression of protein kinase C-delta (PKC-δ), has previously been shown to regulate diverse anorexigenic signals. Here, we demonstrate that these neurons regulate development of activity-based anorexia (ABA), a common animal model for AN. PKC-δ neurons are located in two nuclei of the central extended amygdala (EAc): the central nucleus (CeA) and oval region of the bed nucleus of the stria terminalis (ovBNST). Simultaneous ablation of CeAPKC-δ and ovBNSTPKC-δ neurons prevents ABA, but ablating PKC-δ neurons in the CeA or ovBNST alone is not sufficient. Correspondingly, PKC-δ neurons in both nuclei show increased activity with ABA development. Our study shows how neurons in the amygdala regulate ABA by impacting both feeding and wheel activity behaviors and support a complex heterogeneous etiology of AN.

A revision of the dorsal origin of the frontal aslant tract (FAT) in the superior frontal gyrus: a DWI-tractographic study.

The frontal aslant tract (FAT) is a white matter tract connecting the superior frontal gyrus (SFG) to the inferior frontal gyrus (IFG). Its dorsal origin is identified in humans in the medial wall of the SFG, in the supplementary motor complex (SM-complex). However, empirical observation shows that many FAT fibres appear to originate from the dorsal, rather than medial, portion of the SFG. We quantitatively investigated the actual origin of FAT fibres in the SFG, specifically discriminating between terminations in the medial wall and in the convexity of the SFG. We analysed data from 105 subjects obtained from the Human Connectome Project (HCP) database. We parcelled the cortex of the IFG, dorsal SFG and medial SFG in several regions of interest (ROIs) ordered in a caudal-rostral direction, which served as seed locations for the generation of streamlines. Diffusion imaging data (DWI) was processed using a multi-shell multi-tissue CSD-based algorithm. Results showed that the number of streamlines originating from the dorsal wall of the SFG significantly exceeds those from the medial wall of the SFG. Connectivity patterns between ROIs indicated that FAT sub-bundles are segregated in parallel circuits ordered in a caudal-rostral direction. Such high degree of coherence in the streamline trajectory allows to establish pairs of homologous cortical parcels in the SFG and IFG. We conclude that the frontal origin of the FAT is found in both dorsal and medial surfaces of the superior frontal gyrus.

Sleep deprivation changes frequency-specific functional organization of the resting human brain.

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01-0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.

Beyond network connectivity: A classification approach to brain age prediction with resting-state fMRI.

The brain is a complex, dynamic organ that shows differences in the same subject at various periods. Understanding how brain activity changes across age as a function of the brain networks has been greatly abetted by fMRI. Canonical analysis consists of determining how alterations in connectivity patterns (CPs) of certain regions are affected. An alternative approach is taken here by not considering connectivity but rather features computed from recordings at the regions of interest (ROIs). Using machine learning (ML) we assess how neural signals are altered by and prospectively predictive of age and sex via a methodology that is novel in drawing upon pairwise classification across six decades of subjects' chronological ages. ML is used to answer the equally important questions of what properties of the computed features are most predictive as well as which brain networks are most affected by aging. It was found that there is decreased differentiation among the neural signals of older subjects that are separated in age by the same number of years as younger subjects. Furthermore, the burstiness of the signals change at different rates between males and females. The findings provide insight into brain aging via an ROI-based analysis, the consideration of several feature groups, and a novel classification-based ML pipeline. There is also a contribution to understanding the effects of data aggregated from different recording centers on the conclusions of fMRI studies.

Good heavens! Finally a landslide analysis of basal ganglia circuitry in teleosts.

Tanimoto et al.1 report essential information on teleostean basal ganglia circuitry. This analysis opens gateways into studying neurophysiology, neuropharmacology, and behavior in zebrafish, guided by this complex functional neural system common to all vertebrates.

Brain connectivity patterns derived from aging-related alterations in dynamic brain functional networks and their potential as features for brain age classification.

Objective.Recent studies have demonstrated that the analysis of brain functional networks (BFNs) is a powerful tool for exploring brain aging and age-related neurodegenerative diseases. However, investigating the mechanism of brain aging associated with dynamic BFN is still limited. The purpose of this study is to develop a novel scheme to explore brain aging patterns by constructing dynamic BFN using resting-state functional magnetic resonance imaging data.Approach.A dynamic sliding-windowed non-negative block-diagonal representation (dNBDR) method is proposed for constructing dynamic BFN, based on which a collection of dynamic BFN measures are suggested for examining age-related differences at the group level and used as features for brain age classification at the individual level.Results.The experimental results reveal that the dNBDR method is superior to the sliding time window with Pearson correlation method in terms of dynamic network structure quality. Additionally, significant alterations in dynamic BFN structures exist across the human lifespan. Specifically, average node flexibility and integration coefficient increase with age, while the recruitment coefficient shows a decreased trend. The proposed feature extraction scheme based on dynamic BFN achieved the highest accuracy of 78.7% in classifying three brain age groups.Significance. These findings suggest that dynamic BFN measures, dynamic community structure metrics in particular, play an important role in quantitatively assessing brain aging.

Resting-state brain network connectivity is an independent predictor of responsiveness to language therapy in chronic post-stroke aphasia.

Post-stroke aphasia recovery, especially in the chronic phase, is challenging to predict. Functional integrity of the brain and brain network topology have been suggested as biomarkers of language recovery. This study sought to investigate functional connectivity in four predefined brain networks (i.e., language, default mode, dorsal attention, and salience networks), in relation to aphasia severity and response to language therapy. Thirty patients with chronic post-stroke aphasia were recruited and received a treatment targeting word finding. Structural and functional brain scans were acquired at baseline and resting state functional connectivity for each network was calculated. Additionally, graph measures quantifying network properties were calculated for each network. These included global efficiency for all networks and average strength and clustering coefficient for the language network. Linear mixed effects models showed that mean functional connectivity in the default mode, dorsal attention, and salience networks as well as graph measures of all four networks are independent predictors of response to therapy. While greater mean functional connectivity and global efficiency of the dorsal attention and salience networks predicted greater treatment response, greater mean functional connectivity and global efficiency in the default mode network predicted poorer treatment response. Results for the language network were more nuanced with more efficient network configurations (as reflected in graph measures), but not mean functional connectivity, predicting greater treatment response. These findings highlight the prognostic value of resting-state functional connectivity in chronic treatment-induced aphasia recovery.