Heterogeneity of comprehensive clinical phenotype and longitudinal adaptive function and correlation with computational predictions of severity of missense genotypes in KIF1A-associated neurological disorder.

PURPOSE: Pathogenic variants in Kinesin Family Member 1A (KIF1A) are associated with KIF1A-associated neurological disorder (KAND). We report the clinical phenotypes and correlate genotypes of individuals with KAND. METHODS: Medical history and adaptive function were assessed longitudinally. In-person evaluations included neurological, motor, ophthalmologic and cognitive assessments. RESULTS: We collected online data on 177 individuals. Fifty-seven individuals were also assessed in-person. Most individuals had de novo heterozygous missense likely pathogenic/pathogenic KIF1A variants. The most common characteristics were hypotonia, spasticity, ataxia, seizures, optic nerve atrophy, cerebellar atrophy, and cognitive impairment. Mean Vineland Adaptive Behavior Composite score (VABS-ABC) was low (M=62.9, SD=19.1). The mean change in VABS-ABC over time was -3.1 (SD=7.3). The decline in VABS-ABC was associated with the age at first assessment and abnormal electroencephalogram/seizure. There was a positive correlation between Evolutionary Scale Model (ESM) score for the variants and final VABS-ABC (p=0.003). Abnormal electroencephalogram/seizure, neuroimaging result, and ESM explain 34% of the variance in final VABS-ABC (p<0.001). CONCLUSION: In-person assessment confirmed caregiver report and identified additional visual deficits. Adaptive function declined over time consistent with both the neurodevelopmental and neurodegenerative nature of the condition. Using ESM score assists in predicting phenotype across a wide range of unique variants.

Interaction of interictal epileptiform activity with sleep spindles is associated with cognitive deficits and adverse surgical outcome in pediatric focal epilepsy.

OBJECTIVE: Temporal coordination between oscillations enables intercortical communication and is implicated in cognition. Focal epileptic activity can affect distributed neural networks and interfere with these interactions. Refractory pediatric epilepsies are often accompanied by substantial cognitive comorbidity, but mechanisms and predictors remain mostly unknown. Here, we investigate oscillatory coupling across large-scale networks in the developing brain. METHODS: We analyzed large-scale intracranial electroencephalographic recordings in children with medically refractory epilepsy undergoing presurgical workup (n = 25, aged 3-21 years). Interictal epileptiform discharges (IEDs), pathologic high-frequency oscillations (HFOs), and sleep spindles were detected. Spatiotemporal metrics of oscillatory coupling were determined and correlated with age, cognitive function, and postsurgical outcome. RESULTS: Children with epilepsy demonstrated significant temporal coupling of both IEDs and HFOs to sleep spindles in discrete brain regions. HFOs were associated with stronger coupling patterns than IEDs. These interactions involved tissue beyond the clinically identified epileptogenic zone and were ubiquitous across cortical regions. Increased spatial extent of coupling was most prominent in older children. Poor neurocognitive function was significantly correlated with high IED-spindle coupling strength and spatial extent; children with strong pathologic interactions additionally had decreased likelihood of postoperative seizure freedom. SIGNIFICANCE: Our findings identify pathologic large-scale oscillatory coupling patterns in the immature brain. These results suggest that such intercortical interactions could predict risk for adverse neurocognitive and surgical outcomes, with the potential to serve as novel therapeutic targets to restore physiologic development.

Increased parietal and occipital lobe gyrification predicts conversion to syndromal psychosis in a clinical high-risk cohort.

BACKGROUND: Local gyrification index (lGI), indicative of the degree of cortical folding is a proxy marker for early cortical neurodevelopmental abnormalities. We studied the difference in lGI between those who do and do not convert to psychosis (non-converters) in a clinical high-risk (CHR) cohort, and whether lGI predicts conversion to psychosis. METHODS: Seventy-two CHR participants with attenuated positive symptom syndrome were followed up for two years. The difference in baseline whole-brain lGI was examined on the T1-weighted MRIs between, i)CHR (N = 72) and healthy controls (N = 19), ii)Converters to psychosis (N = 24) and non-converters (N = 48), adjusting for age and sex, on Freesurfer-6.0. The significant cluster obtained in the converters versus non-converters comparison was registered as a region of interest to individual images of all 72 participants and lGI values were extracted from this region. A cox proportional hazards model was applied with these values to study whether lGI predicts conversion to psychosis. RESULTS: lGI was not different between CHR and healthy controls. lGI was increased in converters in the right-sided inferior parietal and lateral occipital areas (corrected cluster-wise-p-value = 0.009, cohen's f = 0.42) compared to non-converters, which significantly increased the risk of onset of psychosis (p = 0.029, hazard ratio = 1.471). CONCLUSIONS: Increased gyrification in the right-sided inferior parietal and lateral occipital area differentiates converters to psychosis in CHR, significantly increasing the risk of conversion to psychosis. This measure may reflect underlying traits in parts of the brain that develop earliest in-utero (parietal and occipital), conferring a heightened vulnerability to convert to syndromal psychosis subsequently.

Brain volumetric correlates of remotely versus in-person administered symbol digit modalities test in multiple sclerosis.

BACKGROUND: Prior studies in multiple sclerosis (MS) support reliability of telehealth-delivered cognitive batteries, although, to date, none have reported relationships of cognitive test performance to neural correlates across administration modalities. In this study we aimed to compare brain-behavior relationships, using the Symbol Digit Modalities Test (SDMT), the most reliable and sensitive cognitive measure in MS, measured from patients seen via telehealth versus in-person. METHODS: SDMT was administered to individuals with MS either in-person (N=60, mean age=39.7) or remotely via video conference (N=51, mean age=47.4). Magnetic resonance imaging (MRI) data was collected in 3-Tesla scanners. Using 3-dimensional T1 images cerebral, cortical, deep gray, cerebral white matter and thalamic nuclei volumes were calculated. Using a meta-analysis approach with an interaction term for participant group, individual regression models were run for each MRI measure having SDMT scores as the outcome variable in each model. In addition, the correlation and average difference between In-person and Remote group associations across the MRI measures were calculated. Finally, for each MRI variable I2 score was quantified to test the heterogeneity between the groups. RESULTS: Administration modality did not affect the association of SDMT performance with MRI measures. Brain tissue volumes showing high associations with the SDMT scores in one group also showed high associations in the other (r = 0.83; 95% CI = [0.07, 0.86]). The average difference between the In-person and the Remote group associations was not significant (ßRemote - ßIn-person = 0.14, 95% CI = [-0.04, 0.34]). Across MRI measures, the average I2 value was 14%, reflecting very little heterogeneity in the relationship of SDMT performance to brain volume. CONCLUSION: We found consistent relationships to neural correlates across in-person and remote SDMT administration modalities. Hence, our study extended the findings of the previous studies demonstrating the feasibility of remote administration of the SDMT.

Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scans of aging and Alzheimer's disease brains.

While MRI contrast agents such as those based on Gadolinium are needed for high-resolution mapping of brain metabolism, these contrast agents require intravenous administration, and there are rising concerns over their safety and invasiveness. Furthermore, non-contrast MRI scans are more commonly performed than those with contrast agents and are readily available for analysis in public databases such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). In this article, we hypothesize that a deep learning model, trained using quantitative steady-state contrast-enhanced structural MRI datasets, in mice and humans, can generate contrast-equivalent information from a single non-contrast MRI scan. The model was first trained, optimized, and validated in mice, and was then transferred and adapted to humans. We observe that the model can substitute for Gadolinium-based contrast agents in approximating cerebral blood volume, a quantitative representation of brain activity, at sub-millimeter granularity. Furthermore, we validate the use of our deep-learned prediction maps to identify functional abnormalities in the aging brain using locally obtained MRI scans, and in the brain of patients with Alzheimer's disease using publicly available MRI scans from ADNI. Since it is derived from a commonly-acquired MRI protocol, this framework has the potential for broad clinical utility and can also be applied retrospectively to research scans across a host of neurological/functional diseases.

A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer's disease.

BACKGROUND: The three core pathologies of Alzheimer's disease (AD) are amyloid pathology, tau pathology, and neurodegeneration. Biomarkers exist for each. Neurodegeneration is often detected by neuroimaging, and we hypothesized that a voxel-based deep learning approach using structural MRI might outperform other neuroimaging methods. METHODS: First, we implement an MRI-based deep learning model, trained with a data augmentation strategy, which classifies Alzheimer's dementia and generates class activation maps. Next, we tested the model in prodromal AD and compared its performance to other biomarkers of amyloid pathology, tau pathology, and neuroimaging biomarkers of neurodegeneration. RESULTS: The model distinguished between controls and AD with high accuracy (AUROC = 0.973) with class activation maps that localized to the hippocampal formation. As hypothesized, the model also outperformed other neuroimaging biomarkers of neurodegeneration in prodromal AD (AUROC = 0.788) but also outperformed biomarkers of amyloid (CSF Aß = 0.702) or tau pathology (CSF tau = 0.682), and the findings are interpreted in the context of AD's known anatomical biology. CONCLUSIONS: The advantages of using deep learning to extract biomarker information from conventional MRIs extend practically, potentially reducing patient burden, risk, and cost.

Convolutional neural network-aided tuber segmentation in tuberous sclerosis complex patients correlates with electroencephalogram.

OBJECTIVE: One of the clinical hallmarks of tuberous sclerosis complex (TSC) is radiologically identified cortical tubers, which are present in most patients. Intractable epilepsy may require surgery, often involving invasive diagnostic procedures such as intracranial electroencephalography (EEG). Identifying the location of the dominant tuber responsible for generating epileptic activities is a critical issue. However, the link between cortical tubers and epileptogenesis is poorly understood. Given this, we hypothesized that tuber voxel intensity may be an indicator of the dominant epileptogenic tuber. Also, via tuber segmentation based on deep learning, we explored whether an automatic quantification of the tuber burden is feasible. METHODS: We annotated tubers from structural magnetic resonance images across 29 TSC subjects, summarized tuber statistics in eight brain lobes, and determined suspected epileptogenic lobes from the same group using EEG monitoring data. Then, logistic regression analyses were performed to demonstrate the linkage between the statistics of cortical tuber and the epileptogenic zones. Furthermore, we tested the ability of a neural network to identify and quantify tuber burden. RESULTS: Logistic regression analyses showed that the volume and count of tubers per lobe, not the mean or variance of tuber voxel intensity, were positively correlated with electrophysiological data. In 47.6% of subjects, the lobe with the largest tuber volume concurred with the epileptic brain activity. A neural network model on the test dataset showed a sensitivity of .83 for localizing individual tubers. The predicted masks from the model correlated highly with the neurologist labels, and thus may be a useful tool for determining tuber burden and searching for the epileptogenic zone. SIGNIFICANCE: We have proven the feasibility of an automatic segmentation of tubers and a derivation of tuber burden across brain lobes. Our method may provide crucial insights regarding the treatment and outcome of TSC patients.

Depression Is Associated With Preserved Cortical Thickness Relative to Apathy in Frontotemporal Dementia.

OBJECTIVES: To understand the differential neuroanatomical substrates underlying apathy and depression in Frontotemporal dementia (FTD). METHODS: T1-MRIs and clinical data of patients with behavioral and aphasic variants of FTD were obtained from an open database. Cortical thickness was derived, its association with apathy severity and difference between the depressed and not depressed were examined with appropriate covariates. RESULTS: Apathy severity was significantly associated with cortical thinning of the lateral parts of the right sided frontal, temporal and parietal lobes. The right sided orbitofrontal, parsorbitalis and rostral anterior cingulate cortex were thicker in depressed compared to patients not depressed. CONCLUSIONS: Greater thickness of right sided ventromedial and inferior frontal cortex in depression compared to patients without depression suggests a possible requisite of gray matter in this particular area for the manifestation of depression in FTD. This study demonstrates a method for deriving neuroanatomical patterns across non-harmonized neuroimaging data in a neurodegenerative disease.

The role of stereo-electroencephalography to localize the epileptogenic zone in children with nonlesional brain magnetic resonance imaging.

OBJECTIVE: This study aimed to assess the clinical outcome and outcome predictive factors in pediatric epilepsy patients evaluated with stereo-electroencephalography (SEEG). METHODS: Thirty-eight patients who underwent SEEG implantation at the Pediatric Epilepsy Center in New York Presbyterian Hospital between June 2014 and December 2019 were enrolled for retrospective chart review. Postoperative seizure outcomes were evaluated in patients with at least 12-months follow up. Meta-analysis was conducted via electronic literature search of data reported from 2000 to 2020 to evaluate significant surgical outcome predictors for SEEG evaluation in the pediatric population. RESULTS: In the current case series of 25 postsurgical patients with long-term follow up, 16 patients (64.0%) were seizure free. An additional 7 patients (28.0%) showed significant seizure improvement and 2 patients (8.0%) showed no change in seizure activity. Patients with nonlesional magnetic resonance imaging (MRI) achieved seizure freedom in 50% (5/10) of cases. By comparison, 73% (11/15) of patients with lesional MRI achieved seizure freedom. Out of 12 studies, 158 pediatric patients were identified for inclusion in a meta-analysis of the effectiveness of SEEG. Seizure freedom was reported 54.4% (n = 86/158) of patients at last follow up. Among patients with nonlesional MRI, 45% (n = 24) achieved seizure freedom compared with patients with lesional MRI findings (61.2%, n:= 60) (p = 0.02). The risk for seizure recurrence was 2.15 times higher [95% confidence interval [CI] 1.06-4.37, p = 0.033] in patients diagnosed with nonlesional focal epilepsy compared to those with lesional epilepsy [ 1.49 (95% CI 1.06-2.114, p = 0.021]. CONCLUSION: Evaluation by SEEG implantation in pediatric epilepsy is effective in localizing the epileptogenic zone with favorable outcome. Presence of a non-lesional brain MRI was associated with lower chances of seizure freedom. Further research is warranted to improve the efficacy of SEEG in localizing the epileptogenic zone in pediatric patients with non-lesional brain MRI.

An exploratory magnetic resonance imaging study of suicidal ideation in individuals at clinical high-risk for psychosis.

Suicide is a major cause of death in psychosis and associated with significant morbidity. Suicidal ideation (SI) is very common in those at clinical high-risk for psychosis (CHR) and predicts later suicide. Despite substantial work on the pathobiology of suicide in schizophrenia, little is known of its neurobiological underpinnings in the CHR or putatively prodromal state. Therefore, in this pilot study, we examined the neurobiology of SI in CHR individuals using structural MRI. Subjects were aged 14-30 and met criteria for the Attenuated Positive Symptom Psychosis-Risk Syndrome (APSS) delineated in the Structured Interview for Psychosis-Risk Syndromes (SIPS). Suicidality was assessed using the Columbia Suicide Severity Rating Scale (C-SSRS). Volumetric MRI scans were obtained on a 3T Phillips scanner. MRI data were available for 69 individuals (19 CHR without SI, 31 CHR with SI and 19 healthy control subjects). CHR individuals with SI had thicker middle temporal and right insular cortices than CHR individuals without SI and healthy control subjects. The location of these findings is consistent with neurobiological findings regarding suicide in syndromal psychosis. These findings underscore the potential for the use of brain imaging biomarkers of suicide risk in CHR individuals.

Right parahippocampal volume deficit in an older population with posttraumatic stress disorder.

BACKGROUND: Posttraumatic Stress Disorder (PTSD) is an increasingly prevalent condition among older adults and may escalate further as the general population including veterans from recent conflicts grow older. Despite growing evidence of higher medical comorbidity, cognitive impairment and dementia, and disability in older individuals with PTSD, there are very few studies examining brain cortical structure in this population. Hence, we examined cortical volumes in a cross-sectional study of veterans and civilians aged ≥50 years, of both sexes and exposed to trauma (interpersonal, combat, non-interpersonal). METHODS: Cortical volumes were obtained from T1-weighted structural MRI and compared between individuals with PTSD and Trauma Exposed Healthy Controls (TEHC) adjusting for age, sex, estimated intracranial volume, depression severity, and time elapsed since trauma exposure. RESULTS: The PTSD group (N = 55) had smaller right parahippocampal gyrus compared to TEHC (N = 36), corrected p(pFWER) = 0.034, with an effect size of 0.75 (Cohen's d), with no significant group differences in other cortical areas. CONCLUSIONS: These findings are different from the structural brain findings reported in studies in younger age groups (larger parahippocampal volume in PTSD patients), suggesting a possible significant change in brain structure as PTSD patients age. These results need replication in longitudinal studies across the age-span to test whether they are neuroanatomical markers representing disease vulnerability, trauma resilience or pathological neurodegeneration associated with cognitive impairment and dementia.

Deep learning improves utility of tau PET in the study of Alzheimer's disease.

INTRODUCTION: Positron emission tomography (PET) imaging targeting neurofibrillary tau tangles is increasingly used in the study of Alzheimer's disease (AD), but its utility may be limited by conventional quantitative or qualitative evaluation techniques in earlier disease states. Convolutional neural networks (CNNs) are effective in learning spatial patterns for image classification. METHODS: 18F-MK6240 (n = 320) and AV-1451 (n = 446) PET images were pooled from multiple studies. We performed iterations with differing permutations of radioligands, heuristics, and architectures. Performance was compared to a standard region of interest (ROI)-based approach on prediction of memory impairment. We visualized attention of the network to illustrate decision making. RESULTS: Overall, models had high accuracy (> 80%) with good average sensitivity and specificity (75% and 82%, respectively), and had comparable or higher accuracy to the ROI standard. Visualizations of model attention highlight known characteristics of tau radioligand binding. DISCUSSION: CNNs could improve tau PET's role in early disease and extend the utility of tau PET across generations of radioligands.

Brain regions vulnerable and resistant to aging without Alzheimer's disease.

'Normal aging' in the brain refers to age-related changes that occur independent of disease, in particular Alzheimer's disease. A major barrier to mapping normal brain aging has been the difficulty in excluding the earliest preclinical stages of Alzheimer's disease. Here, before addressing this issue we first imaged a mouse model and learn that the best MRI measure of dendritic spine loss, a known pathophysiological driver of normal aging, is one that relies on the combined use of functional and structural MRI. In the primary study, we then deployed the combined functional-structural MRI measure to investigate over 100 cognitively-normal people from 20-72 years of age. Next, to cover the tail end of aging, in secondary analyses we investigated structural MRI acquired from cognitively-normal people, 60-84 years of age, who were Alzheimer's-free via biomarkers. Collectively, the results from the primary functional-structural study, and the secondary structural studies revealed that the dentate gyrus is a hippocampal region differentially affected by aging, and that the entorhinal cortex is a region most resistant to aging. Across the cortex, the primary functional-structural study revealed and that the inferior frontal gyrus is differentially affected by aging, however, the secondary structural studies implicated other frontal cortex regions. Together, the results clarify how normal aging may affect the brain and has possible mechanistic and therapeutic implications.

Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging.

Numerous studies have established that estimated brain age constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases. In this work, we curate a large-scale brain MRI data set of healthy individuals, on which we train a uniform deep learning model for brain age estimation. We demonstrate an age estimation accuracy on a hold-out test set (mean absolute error = 4.06 years, r = 0.970) and an independent life span evaluation data set (mean absolute error = 4.21 years, r = 0.960). We further demonstrate the utility of the estimated age in a life span aging analysis of cognitive functions. In summary, we achieve age estimation performance comparable to previous studies, but with a more heterogenous data set confirming the efficacy of this deep learning framework. We also evaluated training with varying age distributions. The analysis of regional contributions to our brain age predictions through multiple analyses, and confirmation of the association of divergence between the estimated and chronological brain age with neuropsychological measures, may be useful in the development and evaluation of similar imaging biomarkers.

Rasmussen Encephalitis: An Update.

Rasmussen encephalitis (RE) is a rare, devastating, progressive pediatric epilepsy. First described 60 years ago, RE continues to present challenges in diagnosis and management. RE causes a unilateral focal epilepsy in children that typically becomes medically refractory, results in significant hemiparesis, and causes progressive cognitive decline. The etiology is a cell-mediated immune attack on one cerebral hemisphere, though the inciting antigen remains unknown. While the underlying histopathology is unilateral and RE is described as "unihemispheric," studies have demonstrated (1) atrophy of the unaffected hemisphere, (2) electroencephalographic abnormalities (slowing and spikes) in the unaffected hemisphere, and (3) cognitive decline referable to the unaffected hemisphere. These secondary contralateral effects likely reflect the impact of uncontrolled epileptic activity (i.e., epileptic encephalopathy). Hemispheric disconnection (HD) renders 70 to 80% of patients seizure free. While it has the potential to limit the influence of seizures and abnormal electrical activity emanating from the pathological hemisphere, HD entails hemiparesis and hemianopia, as well as aphasia for patients with dominant HD. With the recent expansion of available immunomodulatory therapies, there has been interest in identifying an alternative to HD, though evidence for disease modification is limited to date. We review what is known and what remains unknown about RE.

Hippocampal Pathology in Clinical High-Risk Patients and the Onset of Schizophrenia.

BACKGROUND: We examined neuroimaging-derived hippocampal biomarkers in subjects at clinical high risk (CHR) for psychosis to further characterize the pathophysiology of early psychosis. We hypothesized that glutamate hyperactivity, reflected by increased metabolic activity derived from functional magnetic resonance imaging in the CA1 hippocampal subregion and from proton magnetic resonance spectroscopy-derived hippocampal levels of glutamate/glutamine, represents early hippocampal dysfunction in CHR subjects and is predictive of conversion to syndromal psychosis. METHODS: We enrolled 75 CHR individuals with attenuated positive symptom psychosis-risk syndrome as defined by the Structured Interview for Psychosis-risk Syndromes. We used optimized magnetic resonance imaging techniques to measure 3 validated in vivo pathologies of hippocampal dysfunction-focal cerebral blood volume, focal atrophy, and evidence of elevated glutamate concentrations. All patients were imaged at baseline and were followed for up to 2 years to assess for conversion to psychosis. RESULTS: At baseline, compared with control subjects, CHR individuals had high glutamate/glutamine and elevated focal cerebral blood volume on functional magnetic resonance imaging, but only baseline focal hippocampal atrophy predicted progression to syndromal psychosis. CONCLUSIONS: These findings provide evidence that CHR patients with attenuated psychotic symptoms have glutamatergic abnormalities, although only CHR patients who develop syndromal psychosis exhibit focal hippocampal atrophy. Furthermore, these results support the growing evidence that hippocampal dysfunction is an early feature of schizophrenia and related psychotic disorders.

Glutamate Dehydrogenase-Deficient Mice Display Schizophrenia-Like Behavioral Abnormalities and CA1-Specific Hippocampal Dysfunction.

Brain imaging has revealed that the CA1 subregion of the hippocampus is hyperactive in prodromal and diagnosed patients with schizophrenia (SCZ), and that glutamate is a driver of this hyperactivity. Strikingly, mice deficient in the glutamate synthetic enzyme glutaminase have CA1 hypoactivity and a SCZ-resilience profile, implicating glutamate-metabolizing enzymes. To address this further, we examined mice with a brain-wide deficit in the glutamate-metabolizing enzyme glutamate dehydrogenase (GDH), encoded by Glud1, which should lead to glutamate excess due to reduced glutamate metabolism in astrocytes. We found that Glud1-deficient mice have behavioral abnormalities in the 3 SCZ symptom domains, with increased baseline and amphetamine-induced hyperlocomotion as a positive symptom proxy, nest building and social preference as a negative symptom proxy, and reversal/extradimensional set shifting in the water T-maze and contextual fear conditioning as a cognitive symptom proxy. Neuroimaging of cerebral blood volume revealed hippocampal hyperactivity in CA1, which was associated with volume reduction. Parameters of hippocampal synaptic function revealed excess glutamate release and an elevated excitatory/inhibitory balance in CA1. Finally, in a direct clinical correlation using imaging-guided microarray, we found a significant SCZ-associated postmortem reduction in GLUD1 expression in CA1. These findings advance GLUD1 deficiency as a driver of excess hippocampal excitatory transmission and SCZ symptoms, and identify GDH as a target for glutamate modulation pharmacotherapy for SCZ. More broadly, these findings point to the likely involvement of alterations in glutamate metabolism in the pathophysiology of SCZ.