Alik S. Widge

The NOESY jigsaw: automated protein secondary structure and main-chain assignment from sparse, unassigned NMR data.

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the JIGSAW algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). JIGSAW applies graph algorithms and probabilistic reasoning techniques, enforcing first-principles consistency rules in order to overcome a 5-10% signal-to-noise ratio. It consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. Deferring assignment eliminates the bottleneck faced by traditional approaches, which begin by correlating peaks among dozens of experiments. JIGSAW utilizes only four experiments, none of which requires 13C-labeled protein, thus dramatically reducing both the amount and expense of wet lab molecular biology and the total spectrometer time. Results for three test proteins demonstrate that JIGSAW correctly identifies 79-100% of alpha-helical and 46-65% of beta-sheet NOE connectivities and correctly aligns 33-100% of secondary structure elements. JIGSAW is very fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations. It could be useful for quick structural assays to speed data to the biologist early in an investigation and could in principle be applied in an automation-like fashion to a large fraction of the proteome.

The NOESY jigsaw: automated protein secondary structure and main-chain assignment from sparse, unassigned NMR data

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the JIGSAW algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). JIGSAW applies graph algorithms and probabilistic reasoning techniques, enforcing first-principles consistency rules in order to overcome a 5-10% signal-to-noise ratio. It consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. JIGSAWs deferment of assignment until after secondary structure identification differs greatly from traditional approaches, which begin by correlating peaks among dozens of experiments. By deferring assignment, JIGSAW not only eliminates this bottleneck, it also allows the number of experiments to be reduced from dozens to four, none of which requires 13 C-labeled protein. This in turn dramatically reduces the amount and expense of wet lab molecular biology for protein expression and purification, as well as the total spectrometer time to collect data. Our results for three test proteins demonstrate that we are able to identify and align approximately 80 percent of α-helical and 60 percent of β-sheet structure. JIGSAW is very fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations, utilizing a suite of graph analysis algorithms to compensate for the data sparseness. JIGSAW could be used for quick structural assays to speed data to the biologist early in the process of investigation, and could in principle be applied in an automation-like fashion to a large fraction of the proteome.

Staying in the Loop: Relational Agency and Identity in Next-Generation DBS for Psychiatry

In this article, we explore how deep brain stimulation (DBS) devices designed to “close the loop”—to automatically adjust stimulation levels based on computational algorithms—may risk taking the individual agent “out of the loop” of control in areas where (at least apparent) conscious control is a hallmark of our agency. This is of particular concern in the area of psychiatric disorders, where closed-loop DBS is attracting increasing attention as a therapy. Using a relational model of identity and agency, we consider whether DBS designed for psychiatric regulation may require special attention to agency. To do this, we draw on philosophical work on relational identity and agency, connecting it with reports from people using first-generation DBS devices for depression and obsessive-compulsive disorder. We suggest a way to extend a notion of relational agency to encompass neural devices.

Low-Intensity Transcranial Current Stimulation in Psychiatry

Neurostimulation is rapidly emerging as an important treatment modality for psychiatric disorders. One of the fastest-growing and least-regulated approaches to noninvasive therapeutic stimulation involves the application of weak electrical currents. Widespread enthusiasm for low-intensity transcranial electrical current stimulation (tCS) is reflected by the recent surge in direct-to-consumer device marketing, do-it-yourself enthusiasm, and an escalating number of clinical trials. In the wake of this rapid growth, clinicians may lack sufficient information about tCS to inform their clinical practices. Interpretation of tCS clinical trial data is aided by familiarity with basic neurophysiological principles, potential mechanisms of action of tCS, and the complicated regulatory history governing tCS devices. A growing literature includes randomized controlled trials of tCS for major depression, schizophrenia, cognitive disorders, and substance use disorders. The relative ease of use and abundant access to tCS may represent a broad-reaching and important advance for future mental health care. Evidence supports application of one type of tCS, transcranial direct current stimulation (tDCS), for major depression. However, tDCS devices do not have regulatory approval for treating medical disorders, evidence is largely inconclusive for other therapeutic areas, and their use is associated with some physical and psychiatric risks. One unexpected finding to arise from this review is that the use of cranial electrotherapy stimulation devices-the only category of tCS devices cleared for use in psychiatric disorders-is supported by low-quality evidence.

Affective brain-computer interfaces as enabling technology for responsive psychiatric stimulation

There is a pressing clinical need for responsive neurostimulators, which sense a patients brain activity and deliver targeted electrical stimulation to suppress unwanted symptoms. This is particularly true in psychiatric illness, where symptoms can fluctuate throughout the day. Affective BCIs, which decode emotional experience from neural activity, are a candidate control signal for responsive stimulators targeting the limbic circuit. Present affective decoders, however, cannot yet distinguish pathologic from healthy emotional extremes. Indiscriminate stimulus delivery would reduce quality of life and may be actively harmful. We argue that the key to overcoming this limitation is to specifically decode volition, in particular the patients intention to experience emotional regulation. Those emotion-regulation signals already exist in prefrontal cortex (PFC), and could be extracted with relatively simple BCI algorithms. We describe preliminary data from an animal model of PFC-controlled limbic brain stimulation and discuss next steps for pre-clinical testing and possible translation.

Baseline and treatment-emergent EEG biomarkers of antidepressant medication response do not predict response to repetitive transcranial magnetic stimulation.

There has been a surge of interest in biomarkers that can rapidly predict or assess response to psychiatric treatment, as the current standard practice of extended therapeutic trials is often dissatisfying to both clinicians and patients. Electroencephalographic (EEG) biomarkers in particular have been proposed as an inexpensive yet rapid way of determining whether a patient is responding to an intervention, usually before subjective mood improvement occurs. However, even the most well-reported EEG algorithms have not been subjected to independent replication, limiting their clinical generalizability. It is also unclear whether those biomarkers can generalize beyond their original study population, e.g. to patients undergoing somatic treatments for depression. We report here analysis of EEG data from the pivotal OPT-TMS study of transcranial magnetic stimulation (rTMS) for major depressive disorder. In this dataset, previously reported biomarkers of medication response showed no significant correlation with eventual response to rTMS treatment. Furthermore, EEG power in multiple bands measured at baseline and throughout the treatment course did not correlate with or predict either binary (response/nonresponse) or continuous (Hamilton Rating Scale for Depression) outcome measures. While somewhat limited by technical difficulties in data collection, these analyses are adequately powered to detect clinically relevant biomarkers. We believe this highlights a need for wider-scale independent replication of previous EEG biomarkers, both in pharmacotherapy and neuromodulation.

Deep Brain Stimulation for Treatment-Resistant Psychiatric Illnesses: What Has Gone Wrong and What Should We Do Next?

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Psychosis from subthalamic nucleus deep brain stimulator lesion effect

Background: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) in particular is highly effective in relieving symptoms of Parkinsons disease (PD). However, it can also have marked psychiatric side effects, including delirium, mania, and psychosis. The etiologies of those effects are not well-understood, and both surgeons and consulting psychiatrists are in need of treatment strategies. Case Description: Two patients with young onset of PD and without significant prior psychiatric problems presented for bilateral STN DBS when medications became ineffective. Both had uneventful operative courses but developed florid psychosis 1-2 weeks later, before stimulator activation. Neither showed signs of delirium, but both required hospitalization, and one required treatment with a first-generation antipsychotic drug. Use of that drug did not worsen PD symptoms, contrary to usual expectations. Conclusion: These cases describe a previously unreported post-DBS syndrome in which local tissue reaction to lead implantation produces psychosis even without electrical stimulation of subcortical circuits. The lesion effect also appears to have anti-Parkinsonian effects that may allow the safe use of otherwise contraindicated medications. These cases have implications for management of PD DBS patients postoperatively, and may also be relevant as DBS is further used in other brain regions to treat behavioral disorders.

An implantable 64-channel neural interface with reconfigurable recording and stimulation

Next generation implantable medical devices will have the potential to provide more precise and effective therapies through adaptive closed-loop controllers that combine sensing and stimulation across larger numbers of electrode channels. A major challenge in the design of such devices is balancing increased functionality and channel counts with the miniaturization required for implantation within small anatomical spaces. Customized therapies will require adaptive systems capable of tuning which channels are sensed and stimulated to overcome variability in patient-specific needs, surgical placement of electrodes, and chronic physiological responses. In order to address these challenges, we have designed a miniaturized implantable fully-reconfigurable front-end system that is integrated into the distal end of an 8-wire lead, enabling up to 64 electrodes to be dynamically configured for sensing and stimulation. Full reconfigurability is enabled by two custom 32×2 cross-point switch (CPS) matrix ASICs which can route any electrode to either an amplifier with reprogrammable bandwidth and integrated ADC or to one of two independent stimulation channels that can be driven through the lead. The 8-wire circuit includes a digital interface for robust communication as well as a charge-balanced powering scheme for enhanced safety. The system is encased in a hermetic package designed to fit within a 14 mm bur-hole in the skull for neuromodulation of the brain, but could easily be adapted to enhance therapies across a broad spectrum of applications.

Pre-frontal control of closed-loop limbic neurostimulation by rodents using a brain-computer interface.

OBJECTIVE There is great interest in closed-loop neurostimulators that sense and respond to a patients brain state. Such systems may have value for neurological and psychiatric illnesses where symptoms have high intraday variability. Animal models of closed-loop stimulators would aid preclinical testing. We therefore sought to demonstrate that rodents can directly control a closed-loop limbic neurostimulator via a brain-computer interface (BCI). APPROACH We trained rats to use an auditory BCI controlled by single units in prefrontal cortex (PFC). The BCI controlled electrical stimulation in the medial forebrain bundle, a limbic structure involved in reward-seeking. Rigorous offline analyses were performed to confirm volitional control of the neurostimulator. MAIN RESULTS All animals successfully learned to use the BCI and neurostimulator, with closed-loop control of this challenging task demonstrated at 80% of PFC recording locations. Analysis across sessions and animals confirmed statistically robust BCI control and specific, rapid modulation of PFC activity. SIGNIFICANCE Our results provide a preliminary demonstration of a method for emotion-regulating closed-loop neurostimulation. They further suggest that activity in PFC can be used to control a BCI without pre-training on a predicate task. This offers the potential for BCI-based treatments in refractory neurological and mental illness.