General cognitive ability (g) is central to our understanding of human cognition, as it accounts for nearly half of individual differences in performances on diverse cognitive tests. There is growing interest in the brain networks necessary for g because such knowledge could elucidate neural mechanisms of g. Prior work highlighted the association between g and frontoparietal functional networks. However, the specificity of this relationship has been questioned. Moreover, no studies have compared the relative importance of structural and functional networks for g, and most studies have relied on data from neurologically healthy individuals, which limits causal brain-behavior inference. Lesion network mapping (LNM) can overcome such limitations. LNM integrates lesion location and structural and functional brain network data, and allows for inference upon networks necessary for cognitive functions. Here, we used data from three cohorts of patients with focal brain lesions to perform a large-scale LNM study of g. We also compared the relative value of lesion-behavior mapping, and structural and functional LNM, for predicting g across cohorts.

Using data from 402 individuals with chronic, focal brain lesions from the Iowa Neurological Patient Registry, we created a bifactor model to estimate g from the shared variance across neuropsychological tests. To create “cognitive comparisons,” we also estimated the unique aspects of domain-specific abilities (visuospatial processing, memory, and processing speed) by removing domain-general variance from each. Next, we used multivariate lesion-behavior mapping to create statistically weighted maps linking deficits in g and domain-specific abilities to regions of focal brain damage. To perform LNM, the local maxima of the lesion-behavior maps were used as seeds for structural and functional connectivity analyses based on normative diffusion-weighted imaging and resting-state functional connectivity data, respectively. The resulting maps were collapsed using principal components analysis (PCA). We quantified the overlap between each map and the lesion volumes of patients from two validation cohorts (n = 101, n = 100). We used these scores to predict observed g in the validation cohorts while controlling for lesion volume. We also compared the relative predictive value of the lesion-behavior maps, and the structural and functional LNMs.

Lesion-behavior mapping indicated that lesions of left frontal white matter, bilateral frontal operculum/insula, and a region of white matter in the posterior left hemisphere were associated with impairments in g. Across all lesion-behavior mapping and LNM results, only two of the structural LNM maps linked to g were statistically significantly predictive of g in both validation cohorts: a map corresponding to the anterior thalamic radiation, and another corresponding to left frontal pyramidal projections. Both added value beyond lesion-behavior mapping and functional LNM.

The results are notable in several respects: they highlight the importance of structural networks for g, de-emphasize the relevance of functional networks for g, and suggest novel brain circuitry involved in g. Our findings are consistent with animal studies implicating anterior thalamic nuclei in working memory, a cognitive function central to g. Clinically, our study highlights the importance of considering domain-general deficits and the effects of focal lesions on distributed cognitive networks.

Children born very preterm (VPT; <32 weeks gestation) are at increased risk for long-term neurocognitive sequelae such as behavioral problems. These problems may be caused by disrupted brain development, particularly white matter abnormalities that affect network efficiency, as shown via diffusion magnetic resonance imaging (dMRI). There is evidence that short-term interventions for pediatric clinical populations can lead to behavioral improvements as well as associated neuroplasticity. Adapted from a previous parenting intervention for families of young children with traumatic brain injury, the novel Building Better Brains and Behavior (B4) program teaches responsive parenting skills for families of children born preterm. It is hypothesized that parent-reported externalizing symptoms will improve from pre- to postintervention and that these improvements will be mirrored by an increase in neural efficiency.

VPT children between the ages of 3-8 with documented behavioral problems were recruited to participate in a single-arm pilot clinical trial. Families began with a baseline visit in which the Child Behavior Checklist (CBCL) was administered as a measure of behavior problems, and the child underwent dMRI. Parents then participated in the 7-session intervention integrating self-guided, online learning modules with live virtual coaching sessions with a therapist. Twenty three participants enrolled, 15 of which completed the intervention and baseline MRI scan; 4 children were excluded from analysis due to not meeting eligibility criteria, leaving 11 participants for analysis of intervention effects (8 males, Mage=5.42). At program completion, families returned for a follow-up that entailed another CBCL questionnaire and dMRI scan. Eight children completed the post-intervention scan and five were retained for analysis (4 males, Mage=5.83). Imaging data was analyzed using the Brain Connectivity Toolbox, which generated graph theoretical metrics to characterize the topological organization of anatomical networks.

A paired samples t-test showed significant reduction of externalizing behavior problems pre-intervention (M=61.12, SD=10.02) to post-intervention (M=55.00, SD=11.62; f(10)=3.09, p=0.01). At baseline, externalizing behavior problems were positively correlated with normalized clustering coefficient, r(10)=0.59, p=0.04, and small-worldness, r(10)=0.64, p=0.03. Change in externalizing symptoms pre- to post-intervention was positively correlated with baseline global efficiency, r(4)=0.94, p=0.02, and negatively correlated with mean local efficiency, r(4)=-0.89, p=0.03, and normalized characteristic path length, r(4)=-0.89, p=0.03.

Preliminary results indicate that VPT children who exhibit higher levels of externalizing symptoms show higher normalized clustering coefficient (which is expected of networks with less integration), and higher small-worldness (which is unexpected). Greater behavioral improvements were associated with higher baseline characteristic path length as expected, but lower baseline global efficiency; this may indicate that children who had lower global efficiency to begin with benefitted from the intervention the most. Due to the small sample size and lack of corrections for multiple comparisons, these results are not definitive and further research is needed to elucidate associations between structural connectivity and behavioral intervention in children born very preterm.

Cognitive dysfunction and brain structural connectivity alterations have been observed in major depressive disorder (MDD). However, little is known about their interrelation. The present study follows a network approach to evaluate alterations in cognition-related brain structural networks.

Cognitive performance of n = 805 healthy and n = 679 acutely depressed or remitted individuals was assessed using 14 cognitive tests aggregated into cognitive factors. The structural connectome was reconstructed from structural and diffusion-weighted magnetic resonance imaging. Associations between global connectivity strength and cognitive factors were established using linear regressions. Network-based statistics were applied to identify subnetworks of connections underlying these global-level associations. In exploratory analyses, effects of depression were assessed by evaluating remission status-related group differences in subnetwork-specific connectivity. Partial correlations were employed to directly test the complete triad of cognitive factors, depressive symptom severity, and subnetwork-specific connectivity strength.

All cognitive factors were associated with global connectivity strength. For each cognitive factor, network-based statistics identified a subnetwork of connections, revealing, for example, a subnetwork positively associated with processing speed. Within that subnetwork, acutely depressed patients showed significantly reduced connectivity strength compared to healthy controls. Moreover, connectivity strength in that subnetwork was associated to current depressive symptom severity independent of the previous disease course.

Our study is the first to identify cognition-related structural brain networks in MDD patients, thereby revealing associations between cognitive deficits, depressive symptoms, and reduced structural connectivity. This supports the hypothesis that structural connectome alterations may mediate the association of cognitive deficits and depression severity.