Faculty Bibliography

OBJECTIVES: The objective of this study was to evaluate whether patients with surgically refractory medial temporal lobe epilepsy (MTLE) exhibit a distinct pattern of structural network organization involving the temporal lobes and extratemporal regions. METHODS: We retrospectively studied 18 healthy controls and 20 patients with medication refractory unilateral MTLE who underwent anterior temporal lobectomy for treatment of seizures. Patients were classified as seizure-free or not seizure-free at least 1 year after surgery. The presurgical brain connectome was calculated through probabilistic connectivity from MRI-diffusion tensor imaging from 83 anatomically defined regions of interest encompassing the whole brain. The connectivity patterns were analyzed regarding group differences in regional connectivity and network graph properties. RESULTS: Compared with controls, patients exhibited a decrease in connectivity involving ipsilateral thalamocortical regions, with a pathologic increase in ipsilateral medial temporal lobe, insular, and frontal connectivity. Among patients, those not seizure-free exhibited a higher connectivity between structures in 1) the ipsilateral medial and lateral temporal lobe, 2) the ipsilateral medial temporal and parietal lobe, and 3) the contralateral temporal pole and parietal lobe. Patients not seizure-free also exhibited lower small-worldness in the subnetwork within the ipsilateral temporal lobe, with higher subnetwork integration at the expense of segregation. CONCLUSIONS: MTLE is associated with network rearrangement within, but not restricted to, the temporal lobe ipsilateral to the onset of seizures. Networks involving key components of the medial temporal lobe and structures traditionally not removed during surgery may be associated with seizure control after surgical treatment of MTLE.

High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation.

Temporal codes are believed to play important roles in neuronal representation of information. Neuronal ability to classify and learn temporal spiking patterns is thus essential for successful extraction and processing of information. Understanding neuronal learning of temporal code has been complicated, however, by the intrinsic stochasticity of synaptic transmission. Using a computational model of a learning neuron, the tempotron, we studied the effects of synaptic unreliability and short-term dynamics on the neuron's ability to learn spike timing rules. Our results suggest that such a model neuron can learn to classify spike timing patterns even with unreliable synapses, albeit with a significantly reduced success rate. We explored strategies to improve correct spike timing classification and found that firing clustered spike bursts significantly improves learning performance. Furthermore, rapid activity-dependent modulation of synaptic unreliability, implemented with realistic models of dynamic synapses, further improved classification of different burst properties and spike timing modalities. Neuronal models with only facilitating or only depressing inputs exhibited preference for specific types of spike timing rules, but a mixture of facilitating and depressing synapses permitted much improved learning of multiple rules. We tested applicability of these findings to real neurons by considering neuronal learning models with the naturally distributed input release probabilities found in excitatory hippocampal synapses. Our results suggest that spike bursts comprise several encoding modalities that can be learned effectively with stochastic dynamic synapses, and that distributed release probabilities significantly improve learning performance. Synaptic unreliability and dynamics may thus play important roles in the neuron's ability to learn spike timing rules during decoding.

Loss of FMRP causes fragile X syndrome (FXS), but the physiological functions of FMRP remain highly debatable. Here we show that FMRP regulates neurotransmitter release in CA3 pyramidal neurons by modulating action potential (AP) duration. Loss of FMRP leads to excessive AP broadening during repetitive activity, enhanced presynaptic calcium influx, and elevated neurotransmitter release. The AP broadening defects caused by FMRP loss have a cell-autonomous presynaptic origin and can be acutely rescued in postnatal neurons. These presynaptic actions of FMRP are translation independent and are mediated selectively by BK channels via interaction of FMRP with BK channel's regulatory beta4 subunits. Information-theoretical analysis demonstrates that loss of these FMRP functions causes marked dysregulation of synaptic information transmission. FMRP-dependent AP broadening is not limited to the hippocampus, but also occurs in cortical pyramidal neurons. Our results thus suggest major translation-independent presynaptic functions of FMRP that may have important implications for understanding FXS neuropathology.

Sonic hedgehog (SHH), a key regulator of embryonic neurogenesis, signals directly to neural stem cells (NSCs) in the subventricular zone (SVZ) and to astrocytes in the adult mouse forebrain. The specific mechanism by which the GLI2 and GLI3 transcriptional activators (GLI2(A) and GLI3(A)) and repressors (GLI2(R) and GLI3(R)) carry out SHH signaling has not been addressed. We found that the majority of slow-cycling NSCs express Gli2 and Gli3, whereas Gli1 is restricted ventrally and all three genes are downregulated when NSCs transition into proliferating progenitors. Surprisingly, whereas conditional ablation of Smo in postnatal glial fibrillary acidic protein-expressing cells results in cell-autonomous loss of NSCs and a progressive reduction in SVZ proliferation, without an increase in glial cell production, removal of Gli2 or Gli3 does not alter adult SVZ neurogenesis. Significantly, removing Gli3 in Smo conditional mutants largely rescues neurogenesis and, conversely, expression of a constitutive GLI3(R) in the absence of normal Gli2 and Gli3 abrogates neurogenesis. Thus unattenuated GLI3(R) is a primary inhibitor of adult SVZ NSC function. Ablation of Gli2 and Gli3 revealed a minor role for GLI2(R) and little requirement for GLI(A) function in stimulating SVZ neurogenesis. Moreover, we found that similar rules of GLI activity apply to SHH signaling in regulating SVZ-derived olfactory bulb interneurons and maintaining cortical astrocyte function. Namely, fewer superficial olfactory bulb interneurons are generated in the absence of Gli2 and Gli3, whereas astrocyte partial gliosis results from an increase in GLI3(R). Thus precise titration of GLI(R) levels by SHH is critical to multiple functions of adult NSCs and astrocytes.

The homeobox-containing transcription factor Engrailed-2 (En2) is involved in patterning and neuronal differentiation of the midbrain/hindbrain region, where it is prominently expressed. En2 mRNA is also expressed in the adult mouse hippocampus and cerebral cortex, indicating that it might also function in these brain areas. Genome-wide association studies revealed that En2 is a candidate gene for autism spectrum disorders (ASD), and mice devoid of its expression (En2(-/-) mice) display anatomical, behavioral and clinical "autistic-like" features. Since reduced GABAergic inhibition has been proposed as a possible pathogenic mechanism of ASD, we hypothesized that the phenotype of En2(-/-) mice might include defective GABAergic innervation in the forebrain. Here we show that the Engrailed proteins are present in postnatal GABAergic neurons of the mouse hippocampus and cerebral cortex, and adult En2(-/-) mice show reduced expression of GABAergic marker mRNAs in these areas. In addition, reduction in parvalbumin (PV), somatostatin (SOM) and neuropeptide Y (NPY) expressing interneurons is detected in the hippocampus and cerebral cortex of adult En2(-/-) mice. Our results raise the possibility of a link between altered function of En2, anatomical deficits of GABAergic forebrain neurons and the pathogenesis of ASD.

Deformable models have been widely used with success in medical image analysis. They combine bottom-up information derived from image appearance cues, with top-down shape-based constraints within a physics-based formulation. However, in many real world problems the observations extracted from the image data often contain gross errors, which adversely affect the deformation accuracy. To alleviate this issue, we introduce a new family of deformable models that are inspired from compressed sensing, a technique for efficiently reconstructing a signal based on its sparseness in some domain. In this problem, we employ sparsity to represent the outliers or gross errors, and combine it seamlessly with deformable models. The proposed new formulation is applied to the analysis of cardiac motion, using tagged magnetic resonance imaging (tMRI), where the automated tagging line tracking results are very noisy due to the poor image quality. Our new deformable models track the heart motion robustly, and the resulting strains are consistent with those calculated from manual labels.

We introduce a novel algorithm for segmenting the high resolution CT images of the left ventricle (LV), particularly the papillary muscles and the trabeculae. High quality segmentations of these structures are necessary in order to better understand the anatomical function and geometrical properties of LV. These fine structures, however, are extremely challenging to capture due to their delicate and complex nature in both geometry and topology. Our algorithm computes the potential missing topological structures of a given initial segmentation. Using techniques from computational topology, e.g. persistent homology, our algorithm find topological handles which are likely to be the true signal. To further increase accuracy, these proposals are measured by the saliency and confidence from a trained classifier. Handles with high scores are restored in the final segmentation, leading to high quality segmentation results of the complex structures.

Although many studies have identified neural correlates of memory, relatively little is known about the circuit properties connecting single-neuron physiology to behavior. Here we developed a modeling framework to bridge this gap and identify circuit interactions capable of maintaining short-term memory. Unlike typical studies that construct a phenomenological model and test whether it reproduces select aspects of neuronal data, we directly fit the synaptic connectivity of an oculomotor memory circuit to a broad range of anatomical, electrophysiological, and behavioral data. Simultaneous fits to all data, combined with sensitivity analyses, revealed complementary roles of synaptic and neuronal recruitment thresholds in providing the nonlinear interactions required to generate the observed circuit behavior. This work provides a methodology for identifying the cellular and synaptic mechanisms underlying short-term memory and demonstrates how the anatomical structure of a circuit may belie its functional organization.