http://hdl.handle.net/1721.1/7593 2017-07-09T21:40:53Z 2017-07-09T21:40:53Z Iaccarino, Hannah Frances http://hdl.handle.net/1721.1/109020 2017-05-12T06:19:12Z 2017-01-01T00:00:00Z

Gamma frequency entrainment attenuates amyloid load and modifies microglia Iaccarino, Hannah Frances Gamma oscillations (20-50 Hz), a common local field potential signature in many brain regions, are generated by a resonant circuit between fast-spiking (FS)-parvalbumin (PV)-interneurons and pyramidal cells. Changes in gamma oscillations have been observed in several neurological disorders. However, the relationship between gamma oscillations and cellular pathologies of these disorders is unclear. Here, we investigated this relationship using the 5XFAD mouse model of Alzheimer's disease (AD) and found reduced behaviorally driven gamma activity before the onset of plaque formation or evidence of cognitive decline. Because of the early onset of gamma deficits, we aimed to determine if exogenous gamma manipulations could influence disease pathology progression. We discovered that optogenetically driving FS-PV-interneurons at gamma frequency (40 Hz) reduced levels of amyloid-[beta] (A[beta])₁-₄₀ and A[beta] ₁-₄₂ isoforms in the hippocampus of 5XFAD mice. Neither driving FS-PV-interneurons at other frequencies, nor driving excitatory neurons, reduced A[beta] levels. Furthermore, driving FS-PV-interneurons at 40 Hz reduced enlarged endosomes and amyloid precursor protein (APP) cleavage intermediates in hippocampus. Gene expression profiling revealed an induction of microglia specific genes associated with morphological transformation of microglia and increased A[beta] phagocytosis by microglia. Inspired by these observations, we designed a non-invasive light-flickering paradigm that induced 40 Hz activity in visual cortex. The light-flickering paradigm profoundly reduced A[beta]₁-₄₀ and A[beta]₁-₄₂ levels in the visual cortex of pre-depositing mice and mitigated plaque load in aged, depositing mice. A GABAA antagonist completely blocked this effect; further evidence that GABAergic signaling is essential for this neuroprotective gamma activity. Finally, we showed that 40 Hz activity reduced tau phosphorylation in the TauP301S mouse model. Overall, our findings uncover a previously unappreciated function of the brain's gamma rhythms in neuroprotection by recruiting both neuronal and glial responses to mitigate AD-associated pathology. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, February 2017.; Cataloged from PDF version of thesis. "January 2016."; Includes bibliographical references (pages 101-107).

2017-01-01T00:00:00Z Voigts, Jakob http://hdl.handle.net/1721.1/108887 2017-05-12T06:20:34Z 2017-01-01T00:00:00Z

The role of cortical layer six in the perception and laminar representation of sensory change Voigts, Jakob Neocortex learns predictive models of sensory input, allowing mammals to anticipate future events. A fundamental component of this process is the comparison between expected and actual sensory input, and the layered architecture of neocortex is presumably central to this computation. In this thesis, I examine the role of laminar differences, and specifically the role of layer 6 (L6) in the encoding and perception of stimuli that deviate from previous patterns. In awake mice, layer 4 neurons encode current stimulus deviations with a predominantly monotonic, faithful encoding, while neurons in layer 2/3 encode history dependent change signals with heterogeneous receptive fields. Corticothalamic (CT) cells in Layer 6 respond sparsely, but faithfully encode stimulus identity. Weak optogenetic drive of L6 CT cells disrupted this encoding in layer 6 without affecting overall firing rates. This manipulation also caused layer 2/3 to represent only current stimuli. In a head-fixed stimulus detection task, small stimulus deviations typically make stimuli more detectable, and the L6 manipulation removed this effect, without affecting detection of non-changing stimuli. Analogously, in free sensory decision making behavior, the manipulation selectively impaired perception of deviant stimuli, without affecting basic performance. In contrast, stronger L6 drive reduced sensory gain and impaired tactile sensitivity. These results show an explicit laminar encoding of stimulus changes, and that L6 can play a role in the perception of sensory changes by modulating responses depending on previous, or expected input. This finding provides a new perspective on how the layered cortical architecture can implement computations on hierarchical models of the world. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, February 2017.; Cataloged from PDF version of thesis. "September 2016."; Includes bibliographical references.

2017-01-01T00:00:00Z Stoppel, Laura J. (Laura Jane) http://hdl.handle.net/1721.1/108884 2017-05-12T06:20:30Z 2017-01-01T00:00:00Z

Targeting troubled translation : investigating novel therapeutic targets in mouse models of fragile X and 16p1 1.2 deletion syndrome Stoppel, Laura J. (Laura Jane) in 68 children born in the United States meets the diagnostic criteria for Autism Spectrum Disorder (ASD), a psychiatric illness that shares a high comorbidity with intellectual disability (ID). Despite the high prevalence of ASD, there are currently no mechanism-based treatments available due to a lack of understanding of the pathophysiological processes in the brain that disrupt behavior in affected individuals. Identifying convergent molecular pathways involved in known genetic causes of ASD and ID may broaden our understanding of these disorders and help advance potential targeted treatments for ASD. Synaptic protein synthesis is essential for modification of the brain through experience and is altered in several genetically-defined disorders, notably fragile X (FX), a heritable cause of ASD and ID. Neural activity directs local protein synthesis via activation of metabotropic glutamate receptor 5 (mGlu₅), yet the mechanism by which mGlu₅ couples to the intracellular signaling pathways that regulate synaptic mRNA translation is poorly understood. In this dissertation, we show that manipulation of two novel targets, [beta]-arrestin2 and glycogen synthase kinase 3[alpha] (GSK3[alpha]) are able to independently modulate translation downstream of mGlu₅ Avoiding dose-limiting consequences and unwanted side effects of globally targeting mGlu₅ signaling, pharmacological inhibition of these targets has the potential to provide significant advantages over first-generation mGlu₅ inhibitors for the treatment of FX. Finally, we show that a mouse model of 16p1 1.2 microdeletion disorder, a polygenic disorder known to confer risk for ASD and ID in humans, shares common features of synaptic dysfunction downstream of mGlu₅ with the Fmr KO mouse. Chronic administration of pharmaceutical agents previously shown to restore synaptic function in the Fmr KO mouse successfully corrected many biochemical, cognitive and behavioral impairments in 16p1 1 .2 df/+ mice supporting the hypothesis that troubled translation downstream of mGlu₅ may be a convergent point of dysfunction between these two genetically-defined disorders. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, February 2017.; Cataloged from PDF version of thesis. "September 2016." Vita.; Includes bibliographical references (pages 189-220).

2017-01-01T00:00:00Z Mutch, James Vincent http://hdl.handle.net/1721.1/108881 2017-05-12T06:20:26Z 2017-01-01T00:00:00Z

An invariance-based account of feedforward categorization in a realistic model of the ventral visual pathway Mutch, James Vincent For the recognition of general objects in natural scenes, the current top-performing computer vision models owe a debt to visual neuroscience. The hierarchical architecture of convolutional networks, and related models such as HMAX, mimics that of the ventral stream of visual cortex. In essence, they apply the model of Hubel and Wiesel recursively, alternating layers of 'simple' cells, which are tuned to certain local features, and 'complex' cells, which pool the outputs of simple cells within a local region. With recent advances in deep learning, for many tasks in vision and speech, emphasis has moved away from so-called 'hand-designed' models and toward big data and high throughput computing, with models learning from millions of labeled examples. Yet CNNs only learn their features - the weights of connections in the network. All other aspects of the network (size, connectivity, response functions, etc.) are unlearned architectural choices made by their designers. Vision has not yet been reduced to a pure learning problem - human insight into the nature of visual problems continues to be important. To design a good vision system, one still has to understand vision. And, as evidenced by performance for many complex visual tasks, natural vision systems still 'understand' vision better than we do; there is still much to be learned from them. Our work is based on the HMAX model, which places greater weight on biological realism. Our goals are threefold: to better understand the ventral stream algorithm, as well as the visual problem it solves, and to improve the performance of artificial vision systems. In this work we take two main approaches. i-theory is an ongoing effort to explain the good performance of hierarchical models in terms of a formal theory of invariance to transformations. We provide a reinterpretation of V1 simple and complex cells in the context of i-theory as computing a high-dimensional, locally translation-invariant signature for the contents of a V1 receptive field. We describe a simple algorithm for learning them which can extend without modification to the learning of higher-order representations for V2 and beyond. The algorithm yields model V1 cells having a good fit to data from several animal species. We also demonstrate that a precondition of i-theory, covariance, can hold in upper layers, even for transformations not anticipated in the training of lower layers. No current hierarchical object recognition model incorporates realistic retinal resolution. Incorporating this detail forces a reevaluation of the role of the ventral stream's feedforward core in the larger task of scene understanding as well as many details of the model itself, particularly with respect to scale. We investigate the optimal shape of the input window used to select a subset of the visual information available in a scene for processing in a single feedforward pass, defined as a region in (x, y, A), the handling of the A dimension within the hierarchy, and the problem of clutter. Our main experimental results are (1) spatial wavelengths too small for the retina to perceive across the entire object do not play a significant role in the no-clutter case, but confer robustness in the presence of clutter, and (2) preservation by the hierarchy of information about the relative scale (distance along A) of feature activations is more important than current models reflect. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2017.; Cataloged from PDF version of thesis. "September 2016."; Includes bibliographical references (pages 115-118).

2017-01-01T00:00:00Z