Injury induces synaptic, circuit and systems reorganization. After unilateral amputation or stroke this functional loss disrupts the interhemispheric interaction between intact and deprived somatomotor cortices to recruit deprived cortex in response to intact limb stimulation. This recruitment has been implicated in enhanced intact sensory function. In other patients, maladaptive consequences such as phantom limb pain can occur. We used unilateral whisker denervation in male and female mice to detect circuitry alterations underlying interhemispheric cortical reorganization. Enhanced synaptic strength from the intact cortex via the corpus callosum (CC) onto deep neurons in deprived primary somatosensory barrel cortex (S1BC) has previously been detected. It was hypothesized that specificity in this plasticity may depend on to which area these neurons projected. Increased connectivity to somatomotor areas such as contralateral S1BC, primary motor cortex (M1) and secondary somatosensory cortex (S2) may underlie beneficial adaptations, while increased connectivity to pain areas like anterior cingulate cortex (ACC) might underlie maladaptive pain phenotypes. Neurons from the deprived S1BC that project to intact S1BC were hyperexcitable, had stronger responses and reduced inhibitory input to CC stimulation. M1-projecting neurons also showed increases in excitability and CC input strength that was offset with enhanced inhibition. S2 and ACC-projecting neurons showed no changes in excitability or CC input. These results demonstrate that subgroups of output neurons undergo dramatic and specific plasticity after peripheral injury. The changes in S1BC projecting neurons likely underlie enhanced reciprocal connectivity of S1BC after unilateral deprivation consistent with the model that interhemispheric takeover supports intact whisker processing. Summary Statement Amputation, peripheral injury and stroke patients experience widespread alterations in neural activity after sensory loss. A hallmark of this reorganization is the recruitment of deprived cortical space which likely aids processing and thus enhances performance on intact sensory systems. Conversely, this recruitment of deprived cortical space has been hypothesized to underlie phenotypes like phantom limb pain and hinder recovery. A mouse model of unilateral denervation detected remarkable specificity in alterations in the somatomotor circuit. These changes underlie increased reciprocal connectivity between intact and deprived cortical hemispheres. This increased connectivity may help explain the enhanced intact sensory processing detected in humans.
Outliers in neuroimaging represent spurious data or the data of unusual phenotypes that deserve special attention such as clinical follow‐up. Outliers have usually been detected in a supervised or semi‐supervised manner for labeled neuroimaging cohorts. There has been much less work using unsupervised outlier detection on large unlabeled cohorts like the UK Biobank brain imaging dataset. Given its large sample size, rare imaging phenotypes within this unique cohort are of interest, as they are often clinically relevant and could be informative for discovering new processes. Here, we developed a two‐level outlier detection and screening methodology to characterize individual outliers from the multimodal MRI dataset of more than 15,000 UK Biobank subjects. In primary screening, using brain ventricles, white matter, cortical thickness, and functional connectivity‐based imaging phenotypes, every subject was parameterized with an outlier score per imaging phenotype. Outlier scores of these imaging phenotypes had good‐to‐excellent test–retest reliability, with the exception of resting‐state functional connectivity (RSFC). Due to the low reliability of RSFC outlier scores, RSFC outliers were excluded from further individual‐level outlier screening. In secondary screening, the extreme outliers (1,026 subjects) were examined individually, and those arising from data collection/processing errors were eliminated. A representative subgroup of 120 subjects from the remaining non‐artifactual outliers were radiologically reviewed, and radiological findings were identified in 97.5% of them. This study establishes an unsupervised framework for investigating rare individual imaging phenotypes within a large neuroimaging cohort.
The UK Biobank (UKB) is a large-scale epidemiological study and its imaging component focuses on the pre-symptomatic participants. Given its large sample size, rare imaging phenotypes within this unique cohort are of interest, as they are often clinically relevant and could be informative for discovering new processes and mechanisms. Identifying these rare phenotypes is often referred to as "anomaly detection", or "outlier detection". However, anomaly detection in neuroimaging has usually been applied in a supervised or semi-supervised manner for clinically defined cohorts of relatively small size. There has been much less work using anomaly detection on large unlabeled cohorts like the UKB. Here we developed a two-level anomaly screening methodology to systematically identify anomalies from ~19,000 UKB subjects. The same method was also applied to ~1,000 young healthy subjects from the Human Connectome Project (HCP). In primary screening, using ventricular, white matter, and gray matter-based imaging phenotypes derived from multimodal MRI, every subject was parameterized with an anomaly score per phenotype to quantitate the degree of abnormality. These anomaly scores were highly robust. Anomaly score distributions of the UKB cohort were all more outlier-prone than the HCP cohort of young adults. The approach enabled the assessments of test-retest reliability via the anomaly scores, which ranged from excellent reliability for ventricular volume, white matter lesion volume, and fractional anisotropy, to good reliability for mean diffusivity and cortical thickness. In secondary screening, the anomalies due to data collection/processing errors were eliminated. A subgroup of the remaining anomalies were radiologically reviewed, and a substantial percentage of them (UKB: 90.1%; HCP: 42.9%) had various brain pathologies such as masses, cysts, white matter lesions, infarcts, encephalomalacia, or prominent sulci. The remaining anomalies of the subgroup had unexplained causes and would be interesting for follow-up. Finally, we show that anomaly detection applied to resting-state functional connectivity did not identify any reliable anomalies, which was attributed to the confounding effects of brain-wide signal variation. Together, this study establishes an unsupervised framework for investigating rare individual imaging phenotypes within large heterogeneous cohorts.
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