Faculty Bibliography

Normalization is a widespread neural computation in both early sensory coding and higher-order processes such as attention and multisensory integration. It has been shown that during decision-making, normalization implements a context-dependent value code in parietal cortex. In this paper we develop a simple differential equations model based on presumed neural circuitry that implements normalization at equilibrium and predicts specific time-varying properties of value coding. Moreover, we show that when parameters representing value are changed, the solution curves change in a manner consistent with normalization theory and experiment. We show that these dynamic normalization models naturally implement a time-discounted normalization over past activity, implying an intrinsic reference-dependence in value coding of a kind seen experimentally. These results suggest that a single network mechanism can explain transient and sustained decision activity, reference dependence through time discounting, and hence emphasizes the importance of a dynamic rather than static view of divisive normalization in neural coding

Sepsis is a medical emergency demanding early diagnosis and tailored antimicrobial therapy. Every hour of delay in initiating effective therapy measurably increases patient mortality. Blood culture is currently the reference standard for detecting bloodstream infection, a multistep process which may take one to several days. Here, we report a novel paradigm for earlier detection and the simultaneous identification of pathogens in spiked blood cultures by means of a metabolomic "fingerprint" of the volatile mixture outgassed by the organisms. The colorimetric sensor array provided significantly faster detection of positive blood cultures than a conventional blood culture system (12.1 h versus 14.9 h, P < 0.001) while allowing for the identification of 18 bacterial species with 91.9% overall accuracy within 2 h of growth detection. The colorimetric sensor array also allowed for discrimination between unrelated strains of methicillin-resistant Staphylococcus aureus, indicating that the metabolomic fingerprint has the potential to track nosocomial transmissions. Altogether, the colorimetric sensor array is a promising tool that offers a new paradigm for diagnosing bloodstream infections.

Whereas many genes associated with intellectual disability (ID) encode synaptic proteins, transcriptional defects leading to ID are less well understood. We studied a large, consanguineous pedigree of Arab origin with seven members affected with ID and mild dysmorphic features. Homozygosity mapping and linkage analysis identified a candidate region on chromosome 17 with a maximum multipoint logarithm of odds score of 6.01. Targeted high-throughput sequencing of the exons in the candidate region identified a homozygous 4-bp deletion (c.169_172delCACT) in the METTL23 (methyltransferase like 23) gene, which is predicted to result in a frameshift and premature truncation (p.His57Valfs*11). Overexpressed METTL23 protein localized to both nucleus and cytoplasm, and physically interacted with GABPA (GA-binding protein transcription factor, alpha subunit). GABP, of which GABPA is a component, is known to regulate the expression of genes such as THPO (thrombopoietin) and ATP5B (ATP synthase, H+ transporting, mitochondrial F1 complex, beta polypeptide) and is implicated in a wide variety of important cellular functions. Overexpression of METTL23 resulted in increased transcriptional activity at the THPO promoter, whereas knockdown of METTL23 with siRNA resulted in decreased expression of ATP5B, thus revealing the importance of METTL23 as a regulator of GABPA function. The METTL23 mutation highlights a new transcriptional pathway underlying human intellectual function.

De novo copy-number variants (CNVs) can cause neuropsychiatric disease, but the degree to which they occur somatically, and during development, is unknown. Single-cell whole-genome sequencing (WGS) in >200 single cells, including >160 neurons from three normal and two pathological human brains, sensitively identified germline trisomy of chromosome 18 but found most (≥ 95%) neurons in normal brain tissue to be euploid. Analysis of a patient with hemimegalencephaly (HMG) due to a somatic CNV of chromosome 1q found unexpected tetrasomy 1q in ∼ 20% of neurons, suggesting that CNVs in a minority of cells can cause widespread brain dysfunction. Single-cell analysis identified large (>1 Mb) clonal CNVs in lymphoblasts and in single neurons from normal human brain tissue, suggesting that some CNVs occur during neurogenesis. Many neurons contained one or more large candidate private CNVs, including one at chromosome 15q13.2-13.3, a site of duplication in neuropsychiatric conditions. Large private and clonal somatic CNVs occur in normal and diseased human brains.

The human neocortex has numerous specialized functional areas whose formation is poorly understood. Here, we describe a 15-base pair deletion mutation in a regulatory element of GPR56 that selectively disrupts human cortex surrounding the Sylvian fissure bilaterally including "Broca's area," the primary language area, by disrupting regional GPR56 expression and blocking RFX transcription factor binding. GPR56 encodes a heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptor required for normal cortical development and is expressed in cortical progenitor cells. GPR56 expression levels regulate progenitor proliferation. GPR56 splice forms are highly variable between mice and humans, and the regulatory element of gyrencephalic mammals directs restricted lateral cortical expression. Our data reveal a mechanism by which control of GPR56 expression pattern by multiple alternative promoters can influence stem cell proliferation, gyral patterning, and, potentially, neocortex evolution.

Speech recognition in noisy conditions is a major challenge for computer systems, but the human brain performs it routinely and accurately. Automatic speech recognition (ASR) systems that are inspired by neuroscience can potentially bridge the performance gap between humans and machines. We present a system for noise-robust isolated word recognition that works by decoding sequences of spikes from a population of simulated auditory feature-detecting neurons. Each neuron is trained to respond selectively to a brief spectrotemporal pattern, or feature, drawn from the simulated auditory nerve response to speech. The neural population conveys the time-dependent structure of a sound by its sequence of spikes. We compare two methods for decoding the spike sequences--one using a hidden Markov model-based recognizer, the other using a novel template-based recognition scheme. In the latter case, words are recognized by comparing their spike sequences to template sequences obtained from clean training data, using a similarity measure based on the length of the longest common sub-sequence. Using isolated spoken digits from the AURORA-2 database, we show that our combined system outperforms a state-of-the-art robust speech recognizer at low signal-to-noise ratios. Both the spike-based encoding scheme and the template-based decoding offer gains in noise robustness over traditional speech recognition methods. Our system highlights potential advantages of spike-based acoustic coding and provides a biologically motivated framework for robust ASR development.

Many animals produce vocal sequences that appear complex. Most researchers assume that these sequences are well characterized as Markov chains (i.e. that the probability of a particular vocal element can be calculated from the history of only a finite number of preceding elements). However, this assumption has never been explicitly tested. Furthermore, it is unclear how language could evolve in a single step from a Markovian origin, as is frequently assumed, as no intermediate forms have been found between animal communication and human language. Here, we assess whether animal taxa produce vocal sequences that are better described by Markov chains, or by non-Markovian dynamics such as the 'renewal process' (RP), characterized by a strong tendency to repeat elements. We examined vocal sequences of seven taxa: Bengalese finches Lonchura striata domestica, Carolina chickadees Poecile carolinensis, free-tailed bats Tadarida brasiliensis, rock hyraxes Procavia capensis, pilot whales Globicephala macrorhynchus, killer whales Orcinus orca and orangutans Pongo spp. The vocal systems of most of these species are more consistent with a non-Markovian RP than with the Markovian models traditionally assumed. Our data suggest that non-Markovian vocal sequences may be more common than Markov sequences, which must be taken into account when evaluating alternative hypotheses for the evolution of signalling complexity, and perhaps human language origins.

BACKGROUND:Gene polymorphisms that affect serotonin signaling modulate reactivity to salient stimuli and risk for emotional disturbances. Here, we hypothesized that these serotonin genes, which have been primarily explored in depressive disorders, could also have important implications for drug addiction, with the potential to reveal important insights into drug symptomatology, severity, and/or possible sequelae such as dysphoria. METHODS:Using an imaging genetics approach, the current study tested in 62 cocaine abusers and 57 healthy controls the separate and combined effects of variations in the serotonin transporter (5-HTTLPR) and monoamine oxidase A (MAOA) genes on processing of aversive information. Reactivity to standardized unpleasant images was indexed by a psychophysiological marker of stimulus salience (i.e., the late positive potential (LPP) component of the event-related potential) during passive picture viewing. Depressive symptomatology was assessed with the Beck Depression Inventory (BDI). RESULTS:Results showed that, independent of diagnosis, the highest unpleasant LPPs emerged in individuals with MAOA-Low and at least one 'Short' allele of 5-HTTLPR. Uniquely in the cocaine participants with these two risk variants, higher unpleasant LPPs correlated with higher BDI scores. CONCLUSIONS:Taken together, these results suggest that a multilocus genetic composite of monoamine signaling relates to depression symptomatology through brain function associated with the experience of negative emotions. This research lays the groundwork for future studies that can investigate clinical outcomes and/or pharmacogenetic therapies in drug addiction and potentially other psychopathologies of emotion dysregulation.

Functional neuroimaging studies have long implicated the mid-cingulate cortex (MCC) in conflict monitoring, but it is not clear whether its structural integrity (i.e., the gray matter volume) influences its conflict monitoring function. In this multimodal study, we used T1-weighted MRI scans as well as event-related potentials (ERPs) to test whether the MCC gray matter volume is associated with the electrocortical marker (i.e., No-go N200 ERP component) of conflict monitoring in healthy individuals. The specificity of such a relationship in health was determined in two ways: by (A) acquiring the same data from individuals with cocaine use disorder (CUD), known to have deficits in executive function including behavioral monitoring; and (B) acquiring the P300 ERP component that is linked with attention allocation and not specifically with conflict monitoring. Twenty-five (39.1 ± 8.4 years; 8 females) healthy individuals and 25 (42.7 ± 5.9 years; 6 females) individuals with CUD underwent a rewarded Go/No-go task during which the ERP data was collected, and they also underwent a structural MRI scan. The whole brain regression analysis showed a significant correlation between MCC structural integrity and the well-known ERP measure of conflict monitoring (N200, but not the P300) in healthy individuals, which was absent in CUD who were characterized by reduced MCC gray matter volume, N200 abnormalities as well as reduced task accuracy. In individuals with CUD instead, the N200 amplitude was associated with drug addiction symptomatology. These results show that the integrity of MCC volume is directly associated with the electrocortical correlates of conflict monitoring in healthy individuals, and such an association breaks down in psychopathologies that impact these brain processes. Taken together, this MCC-N200 association may serve as a biomarker of improved behavioral monitoring processes in diseased populations.