Pathogenic variants in autism gene KATNAL2 cause hydrocephalus and disrupt neuronal connectivity by impairing ciliary microtubule dynamics.

Enlargement of the cerebrospinal fluid (CSF)-filled brain ventricles (cerebral ventriculomegaly), the cardinal feature of congenital hydrocephalus (CH), is increasingly recognized among patients with autism spectrum disorders (ASD). KATNAL2, a member of Katanin family microtubule-severing ATPases, is a known ASD risk gene, but its roles in human brain development remain unclear. Here, we show that nonsense truncation of Katnal2 (Katnal2Δ17) in mice results in classic ciliopathy phenotypes, including impaired spermatogenesis and cerebral ventriculomegaly. In both humans and mice, KATNAL2 is highly expressed in ciliated radial glia of the fetal ventricular-subventricular zone as well as in their postnatal ependymal and neuronal progeny. The ventriculomegaly observed in Katnal2Δ17 mice is associated with disrupted primary cilia and ependymal planar cell polarity that results in impaired cilia-generated CSF flow. Further, prefrontal pyramidal neurons in ventriculomegalic Katnal2Δ17 mice exhibit decreased excitatory drive and reduced high-frequency firing. Consistent with these findings in mice, we identified rare, damaging heterozygous germline variants in KATNAL2 in five unrelated patients with neurosurgically treated CH and comorbid ASD or other neurodevelopmental disorders. Mice engineered with the orthologous ASD-associated KATNAL2 F244L missense variant recapitulated the ventriculomegaly found in human patients. Together, these data suggest KATNAL2 pathogenic variants alter intraventricular CSF homeostasis and parenchymal neuronal connectivity by disrupting microtubule dynamics in fetal radial glia and their postnatal ependymal and neuronal descendants. The results identify a molecular mechanism underlying the development of ventriculomegaly in a genetic subset of patients with ASD and may explain persistence of neurodevelopmental phenotypes in some patients with CH despite neurosurgical CSF shunting.

Where do we stand on fMRI in awake mice?

Imaging awake animals is quickly gaining traction in neuroscience as it offers a means to eliminate the confounding effects of anesthesia, difficulties of inter-species translation (when humans are typically imaged while awake), and the inability to investigate the full range of brain and behavioral states in unconscious animals. In this systematic review, we focus on the development of awake mouse blood oxygen level dependent functional magnetic resonance imaging (fMRI). Mice are widely used in research due to their fast-breeding cycle, genetic malleability, and low cost. Functional MRI yields whole-brain coverage and can be performed on both humans and animal models making it an ideal modality for comparing study findings across species. We provide an analysis of 30 articles (years 2011-2022) identified through a systematic literature search. Our conclusions include that head-posts are favorable, acclimation training for 10-14 d is likely ample under certain conditions, stress has been poorly characterized, and more standardization is needed to accelerate progress. For context, an overview of awake rat fMRI studies is also included. We make recommendations that will benefit a wide range of neuroscience applications.

A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth.

Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate functional connectome-based markers of attention. In dataset 1, we use connectome-based predictive modeling and observe successful prediction of performance on an in-scan sustained attention task in a sample of youth, including participants with a neurodiverse condition. The predictions are not driven by confounds, such as head motion. In dataset 2, we find that the attention network model defined in dataset 1 generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets 3-5, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attentional state in youth and support the further development of predictive dimensional models of other clinically relevant phenotypes.

Simultaneous cortex-wide fluorescence Ca2+ imaging and whole-brain fMRI.

Achieving a comprehensive understanding of brain function requires multiple imaging modalities with complementary strengths. We present an approach for concurrent widefield optical and functional magnetic resonance imaging. By merging these modalities, we can simultaneously acquire whole-brain blood-oxygen-level-dependent (BOLD) and whole-cortex calcium-sensitive fluorescent measures of brain activity. In a transgenic murine model, we show that calcium predicts the BOLD signal, using a model that optimizes a gamma-variant transfer function. We find consistent predictions across the cortex, which are best at low frequency (0.009-0.08 Hz). Furthermore, we show that the relationship between modality connectivity strengths varies by region. Our approach links cell-type-specific optical measurements of activity to the most widely used method for assessing human brain function.

SMC5 Plays Independent Roles in Congenital Heart Disease and Neurodevelopmental Disability.

Up to 50% of patients with severe congenital heart disease (CHD) develop life-altering neurodevelopmental disability (NDD). It has been presumed that NDD arises in CHD cases because of hypoxia before, during, or after cardiac surgery. Recent studies detected an enrichment in de novo mutations in CHD and NDD, as well as significant overlap between CHD and NDD candidate genes. However, there is limited evidence demonstrating that genes causing CHD can produce NDD independent of hypoxia. A patient with hypoplastic left heart syndrome and gross motor delay presented with a de novo mutation in SMC5. Modeling mutation of smc5 in Xenopus tropicalis embryos resulted in reduced heart size, decreased brain length, and disrupted pax6 patterning. To evaluate the cardiac development, we induced the conditional knockout (cKO) of Smc5 in mouse cardiomyocytes, which led to the depletion of mature cardiomyocytes and abnormal contractility. To test a role for Smc5 specifically in the brain, we induced cKO in the mouse central nervous system, which resulted in decreased brain volume, and diminished connectivity between areas related to motor function but did not affect vascular or brain ventricular volume. We propose that genetic factors, rather than hypoxia alone, can contribute when NDD and CHD cases occur concurrently.

Arousal impacts distributed hubs modulating the integration of brain functional connectivity.

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.

Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders.

Memory deficits are observed in a range of psychiatric disorders, but it is unclear whether memory deficits arise from a shared brain correlate across disorders or from various dysfunctions unique to each disorder. Connectome-based predictive modeling is a computational method that captures individual differences in functional connectomes associated with behavioral phenotypes such as memory. We used publicly available task-based functional MRI data from patients with schizophrenia (n = 33), bipolar disorder (n = 34), attention deficit hyper-activity disorder (n = 32), and healthy controls (n = 73) to model the macroscale brain networks associated with working, short- and long-term memory. First, we use 10-fold and leave-group-out analyses to demonstrate that the same macroscale brain networks subserve memory across diagnostic groups and that individual differences in memory performance are related to individual differences within networks distributed throughout the brain, including the subcortex, default mode network, limbic network, and cerebellum. Next, we show that diagnostic groups are associated with significant differences in whole-brain functional connectivity that are distinct from the predictive models of memory. Finally, we show that models trained on the transdiagnostic sample generalize to novel, healthy participants (n = 515) from the Human Connectome Project. These results suggest that despite significant differences in whole-brain patterns of functional connectivity between diagnostic groups, the core macroscale brain networks that subserve memory are shared.

Resample aggregating improves the generalizability of connectome predictive modeling.

It is a longstanding goal of neuroimaging to produce reliable, generalizable models of brain behavior relationships. More recently, data driven predictive models have become popular. However, overfitting is a common problem with statistical models, which impedes model generalization. Cross validation (CV) is often used to estimate expected model performance within sample. Yet, the best way to generate brain behavior models, and apply them out-of-sample, on an unseen dataset, is unclear. As a solution, this study proposes an ensemble learning method, in this case resample aggregating, encompassing both model parameter estimation and feature selection. Here we investigate the use of resampled aggregated models when used to estimate fluid intelligence (fIQ) from fMRI based functional connectivity (FC) data. We take advantage of two large openly available datasets, the Human Connectome Project (HCP), and the Philadelphia Neurodevelopmental Cohort (PNC). We generate aggregated and non-aggregated models of fIQ in the HCP, using the Connectome Prediction Modelling (CPM) framework. Over various test-train splits, these models are evaluated in sample, on left-out HCP data, and out-of-sample, on PNC data. We find that a resample aggregated model performs best both within- and out-of-sample. We also find that feature selection can vary substantially within-sample. More robust feature selection methods, as detailed here, are needed to improve cross sample performance of CPM based brain behavior models.

Neurovascular unit remodelling in the subacute stage of stroke recovery.

Brain plasticity following focal cerebral ischaemia has been observed in both stroke survivors and in preclinical models of stroke. Endogenous neurovascular adaptation is at present incompletely understood yet its potentiation may improve long-term functional outcome. We employed longitudinal MRI, intracranial array electrophysiology, Montoya Staircase testing, and immunofluorescence to examine function of brain vessels, neurons, and glia in addition to forelimb skilled reaching during the subacute stage of ischemic injury progression. Focal ischemic stroke (~100mm3 or ~20% of the total brain volume) was induced in adult Sprague-Dawley rats via direct injection of endothelin-1 (ET-1) into the right sensori-motor cortex, producing sustained impairment in left forelimb reaching ability. Resting perfusion and vascular reactivity to hypercapnia in the peri-lesional cortex were elevated by approximately 60% and 80% respectively seven days following stroke. At the same time, the normal topological pattern of local field potential (LFP) responses to peripheral somatosensory stimulation was abolished and the average power of spontaneous LFP activity attenuated by approximately 50% relative to the contra-lesional cortex, suggesting initial response attenuation within the peri-infarct zone. By 21 days after stroke, perilesional blood flow resolved, but peri-lesional vascular reactivity remained elevated. Concomitantly, the LFP response amplitudes increased with distance from the site of ET-1 injection, suggesting functional remodelling from the core of the lesion to its periphery. This notion was further buttressed by the lateralization of spontaneous neuronal activity: by day 21, the average ipsi-lesional power of spontaneous LFP activity was almost twice that of the contra-lesional cortex. Over the observation period, the peri-lesional cortex exhibited increased vascular density, along with neuronal loss, astrocytic activation, and recruitment and activation of microglia and macrophages, with neuronal loss and inflammation extending beyond the peri-lesional cortex. These findings highlight the complex relationship between neurophysiological state and behaviour and provide evidence of highly dynamic functional changes in the peri-infarct zone weeks following the ischemic insult, suggesting an extended temporal window for therapeutic interventions.

Modulation of the peri-infarct neurogliovascular function by delayed COX-1 inhibition.

PURPOSE: Stroke is the leading cause of adult disability worldwide. The absence of more effective interventions in the chronic stage-that most patients stand to benefit from-reflects uncertainty surrounding mechanisms that govern recovery. The present work investigated the effects of a novel treatment (selective cyclooxygenase-1, COX-1, inhibition) in a model of focal ischemia. MATERIALS AND METHODS: FR122047 (COX-1 inhibitor) was given beginning 7 days following stroke (cortical microinjection of endothelin-1) in 23 adult male rats. Longitudinal continuous-arterial-spin-labeling was performed prior to treatment (7 days), and repeated following treatment (21 days) on a 7T magnetic resonance imaging (MRI) system to estimate resting perfusion and reactivity to hypercapnia. These in vivo measurements were buttressed by immunohistochemistry. RESULTS: Stroke caused an increase in perilesional resting perfusion (peri-/contralesional perfusion ratio of 170 ± 10%) and perfusion responses to hypercapnia (180 ± 10%) at 7 days. At 21 days, placebo-administered rats showed normalized perilesional perfusion (100 ± 20%) but persistent hyperreactivity (190 ± 20%). Treated animals exhibited sustained perilesional hyperperfusion (180 ± 10%). Further, reactivity lateralization did not persist following treatment (peri- vs. contralesional reactivity: P = 0.002 at 7 vs. P = 0.2 at 21 days). Hemodynamic changes were accompanied by neuronal loss, increased endothelial density, and widespread microglial and astrocytic activation. Moreover, relative to controls, treated rats showed increased perilesional neuronal survival (22 ± 1% vs. 14.9 ± 0.8%, P = 0.02) and decreased microglia/macrophage recruitment (17 ± 1% vs. 20 ± 1%, P = 0.05). Finally, perilesional perfusion was correlated with neuronal survival (slope = 0.14 ± 0.05; R2 = 0.7, P = 0.03). CONCLUSION: These findings shed light on the role of COX-1 in chronic ischemic injury and suggest that delayed selective COX-1 inhibition exerts multiple beneficial effects on the neurogliovascular unit. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 4 J. MAGN. RESON. IMAGING 2017;46:505-517.

Altered intracortical myelin staining in the dorsolateral prefrontal cortex in severe mental illness.

Imaging and postmortem studies into the severe mental illnesses of major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ) have revealed deficiencies in the myelination of deep white matter tracts of the brain. Recent studies have further suggested that deficits could extend to myelinated fibers running through the cortex in those illnesses. Disruptions in this intracortical myelin may underlie functional symptomology in MDD, BD, and SZ; thus, in this study, we hypothesized that individuals with these illnesses may have reduced myelin staining relative to controls in the cerebral cortex. We stained 60 sections of dorsolateral prefrontal cortex for myelin with Luxol® fast blue in four groups: 15 BD, 15 MDD, 15 SZ, and 15 controls with no psychiatric illness. We digitally measured optical tissue attenuation reflecting the amount of myelin staining across six cortical depths in the middle frontal gyrus (MFG), in superficial white matter in the crown of the MFG, and in deep white matter. We found that a diagnosis of MDD or SZ meant that optical tissue attenuation was significantly reduced in the shallowest depths of the cortex. Furthermore, there was a trend toward reduced optical tissue attenuation in all illnesses across all myelinated regions we studied. These results encourage future studies into potential reductions in intracortical myelin in severe mental illness.

What N Is N-ough for MRI-Based Animal Neuroimaging?

Fueled by the recent and controversial brain-wide association studies in humans, the animal neuroimaging community has also begun questioning whether using larger sample sizes is necessary for ethical and effective scientific progress. In this opinion piece, we illustrate two opposing views on sample size extremes in MRI-based animal neuroimaging.

Multimodal identification of the mouse brain using simultaneous Ca 2+ imaging and fMRI.

Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the workhorse in this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome- based identification to be successful and explored various features of these data.

Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization.

Large-scale functional networks have been characterized in both rodent and human brains, typically by analyzing fMRI-BOLD signals. However, the relationship between fMRI-BOLD and underlying neural activity is complex and incompletely understood, which poses challenges to interpreting network organization obtained using this technique. Additionally, most work has assumed a disjoint functional network organization (i.e., brain regions belong to one and only one network). Here, we employ wide-field Ca2+ imaging simultaneously with fMRI-BOLD in mice expressing GCaMP6f in excitatory neurons. We determine cortical networks discovered by each modality using a mixed-membership algorithm to test the hypothesis that functional networks exhibit overlapping organization. We find that there is considerable network overlap (both modalities) in addition to disjoint organization. Our results show that multiple BOLD networks are detected via Ca2+ signals, and networks determined by low-frequency Ca2+ signals are only modestly more similar to BOLD networks. In addition, the principal gradient of functional connectivity is nearly identical for BOLD and Ca2+ signals. Despite similarities, important differences are also detected across modalities, such as in measures of functional connectivity strength and diversity. In conclusion, Ca2+ imaging uncovers overlapping functional cortical organization in the mouse that reflects several, but not all, properties observed with fMRI-BOLD signals.

Autism spectrum disorder-specific changes in white matter connectome edge density based on functionally defined nodes.

Introduction: Autism spectrum disorder (ASD) is associated with both functional and microstructural connectome disruptions. We deployed a novel methodology using functionally defined nodes to guide white matter (WM) tractography and identify ASD-related microstructural connectome changes across the lifespan. Methods: We used diffusion tensor imaging and clinical data from four studies in the national database for autism research (NDAR) including 155 infants, 102 toddlers, 230 adolescents, and 96 young adults - of whom 264 (45%) were diagnosed with ASD. We applied cortical nodes from a prior fMRI study identifying regions related to symptom severity scores and used these seeds to construct WM fiber tracts as connectome Edge Density (ED) maps. Resulting ED maps were assessed for between-group differences using voxel-wise and tract-based analysis. We then examined the association of ASD diagnosis with ED driven from functional nodes generated from different sensitivity thresholds. Results: In ED derived from functionally guided tractography, we identified ASD-related changes in infants (pFDR ≤ 0.001-0.483). Overall, more wide-spread ASD-related differences were detectable in ED based on functional nodes with positive symptom correlation than negative correlation to ASD, and stricter thresholds for functional nodes resulted in stronger correlation with ASD among infants (z = -6.413 to 6.666, pFDR ≤ 0.001-0.968). Voxel-wise analysis revealed wide-spread ED reductions in central WM tracts of toddlers, adolescents, and adults. Discussion: We detected early changes of aberrant WM development in infants developing ASD when generating microstructural connectome ED map with cortical nodes defined by functional imaging. These were not evident when applying structurally defined nodes, suggesting that functionally guided DTI-based tractography can help identify early ASD-related WM disruptions between cortical regions exhibiting abnormal connectivity patterns later in life. Furthermore, our results suggest a benefit of involving functionally informed nodes in diffusion imaging-based probabilistic tractography, and underline that different age cohorts can benefit from age- and brain development-adapted image processing protocols.

Consciousness: Mapping the awake mouse brain.

Brain activity during consciousness has a unique spatiotemporal signature that is evolutionarily conserved. A recent study uses functional magnetic resonance imaging to show how spontaneous activity in the murine brain reconfigures with wakefulness.

Building bridges: simultaneous multimodal neuroimaging approaches for exploring the organization of brain networks.

Brain organization is evident across spatiotemporal scales as well as from structural and functional data. Yet, translating from micro- to macroscale (vice versa) as well as between different measures is difficult. Reconciling disparate observations from different modes is challenging because each specializes within a restricted spatiotemporal milieu, usually has bounded organ coverage, and has access to different contrasts. True intersubject biological heterogeneity, variation in experiment implementation (e.g., use of anesthesia), and true moment-to-moment variations in brain activity (maybe attributable to different brain states) also contribute to variability between studies. Ultimately, for a deeper and more actionable understanding of brain organization, an ability to translate across scales, measures, and species is needed. Simultaneous multimodal methods can contribute to bettering this understanding. We consider four modes, three optically based: multiphoton imaging, single-photon (wide-field) imaging, and fiber photometry, as well as magnetic resonance imaging. We discuss each mode as well as their pairwise combinations with regard to the definition and study of brain networks.

Functional Connectome-Based Predictive Modeling in Autism.

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

Age-dependent white matter microstructural disintegrity in autism spectrum disorder.

There has been increasing evidence of White Matter (WM) microstructural disintegrity and connectome disruption in Autism Spectrum Disorder (ASD). We evaluated the effects of age on WM microstructure by examining Diffusion Tensor Imaging (DTI) metrics and connectome Edge Density (ED) in a large dataset of ASD and control patients from different age cohorts. N = 583 subjects from four studies from the National Database of Autism Research were included, representing four different age groups: (1) A Longitudinal MRI Study of Infants at Risk of Autism [infants, median age: 7 (interquartile range 1) months, n = 155], (2) Biomarkers of Autism at 12 months [toddlers, 32 (11)m, n = 102], (3) Multimodal Developmental Neurogenetics of Females with ASD [adolescents, 13.1 (5.3) years, n = 230], (4) Atypical Late Neurodevelopment in Autism [young adults, 19.1 (10.7)y, n = 96]. For each subject, we created Fractional Anisotropy (FA), Mean- (MD), Radial- (RD), and Axial Diffusivity (AD) maps as well as ED maps. We performed voxel-wise and tract-based analyses to assess the effects of age, ASD diagnosis and sex on DTI metrics and connectome ED. We also optimized, trained, tested, and validated different combinations of machine learning classifiers and dimensionality reduction algorithms for prediction of ASD diagnoses based on tract-based DTI and ED metrics. There is an age-dependent increase in FA and a decline in MD and RD across WM tracts in all four age cohorts, as well as an ED increase in toddlers and adolescents. After correction for age and sex, we found an ASD-related decrease in FA and ED only in adolescents and young adults, but not in infants or toddlers. While DTI abnormalities were mostly limited to the corpus callosum, connectomes showed a more widespread ASD-related decrease in ED. Finally, the best performing machine-leaning classification model achieved an area under the receiver operating curve of 0.70 in an independent validation cohort. Our results suggest that ASD-related WM microstructural disintegrity becomes evident in adolescents and young adults-but not in infants and toddlers. The ASD-related decrease in ED demonstrates a more widespread involvement of the connectome than DTI metrics, with the most striking differences being localized in the corpus callosum.