mood disorder
Research Papers
Connectome-wide search for functional connectivity locus associated with pathological rumination as a target for real-time fMRI neurofeedback intervention
Real-time fMRI neurofeedback (rtfMRI-nf) enables noninvasive targeted intervention in brain activation with high spatial specificity. To achieve this promise of rtfMRI-nf, we introduced and demonstrated a data-driven framework to design a rtfMRI-nf intervention through the discovery of precise target location associated with clinical symptoms and neurofeedback signal optimization. Specifically, we identified the functional connectivity locus associated with rumination symptoms, utilizing a connectome-wide search in resting-state fMRI data from a large cohort of mood and anxiety disorder individuals (N = 223) and healthy controls (N = 45). Then, we performed a rtfMRI simulation analysis to optimize the online functional connectivity neurofeedback signal for the identified functional connectivity. The connectome-wide search was performed in the medial prefrontal cortex and the posterior cingulate cortex/precuneus brain regions to identify the precise location of the functional connectivity associated with rumination severity as measured by the ruminative response style (RRS) scale. The analysis found that the functional connectivity between the loci in the precuneus (-6, −54, 48 mm in MNI) and the right temporo-parietal junction (RTPJ; 49, −49, 23 mm) was positively correlated with RRS scores (depressive, p < 0.001; brooding, p < 0.001; reflective, p = 0.002) in the mood and anxiety disorder group. We then performed a rtfMRI processing simulation to optimize the online computation of the precuneus-RTPJ connectivity. We determined that the two-point method without a control region was appropriate as a functional connectivity neurofeedback signal with less dependence on signal history and its accommodation of head motion. The present study offers a discovery framework for the precise location of functional connectivity targets for rtfMRI-nf intervention, which could help directly translate neuroimaging findings into clinical rtfMRI-nf interventions.
View Full Paper →Neurocognitive therapeutics: from concept to application in the treatment of negative attention bias
There is growing interest in the use of neuroimaging for the direct treatment of mental illness. Here, we present a new framework for such treatment, neurocognitive therapeutics. What distinguishes neurocognitive therapeutics from prior approaches is the use of precise brain-decoding techniques within a real-time feedback system, in order to adapt treatment online and tailor feedback to individuals' needs. We report an initial feasibility study that uses this framework to alter negative attention bias in a small number of patients experiencing significant mood symptoms. The results are consistent with the promise of neurocognitive therapeutics to improve mood symptoms and alter brain networks mediating attentional control. Future work should focus on optimizing the approach, validating its effectiveness, and expanding the scope of targeted disorders.
View Full Paper →Diagnostic and therapeutic utility of neuroimaging in depression: an overview
A growing number of studies have used neuroimaging to further our understanding of how brain structure and function are altered in major depression. More recently, these techniques have begun to show promise for the diagnosis and treatment of depression, both as aids to conventional methods and as methods in their own right. In this review, we describe recent neuroimaging findings in the field that might aid diagnosis and improve treatment accuracy. Overall, major depression is associated with numerous structural and functional differences in neural systems involved in emotion processing and mood regulation. Furthermore, several studies have shown that the structure and function of these systems is changed by pharmacological and psychological treatments of the condition and that these changes in candidate brain regions might predict clinical response. More recently, "machine learning" methods have used neuroimaging data to categorize individual patients according to their diagnostic status and predict treatment response. Despite being mostly limited to group-level comparisons at present, with the introduction of new methods and more naturalistic studies, neuroimaging has the potential to become part of the clinical armamentarium and may improve diagnostic accuracy and inform treatment choice at the patient level.
View Full Paper →The LENS (Low Energy Neurofeedback System): A Clinical Outcomes Study on One Hundred Patients at Stone Mountain Center, New York
Introduction. The Low Energy Neurofeedback System (LENS) developed by Dr. Len Ochs (2006a) uses feedback in the form of a radio frequency carrier wave, administered at a positive offset frequency from the person's own dominant EEG frequency. Although it is an unusual biofeedback procedure, the feedback being invisible and the subject passive, clinical evidence supports the efficacy of the LENS across a spectrum of conditions. Published research studies (Schoenberger, Shifflet, Esty, Ochs, & Matheis, 2001; Donaldson, Sella, & Mueller, 1998; Mueller, Donaldson, Nelson, & Layman, 2001) have shown the effectiveness of the LENS method with traumatic brain injury (TBI) and with fibromyalgia. No study to date has evaluated LENS treatment across the spectrum of disorders and with a significantly large sample. This study was devised to address these issues. The study hypotheses were that the LENS treatment would be effective in reducing both systematic symptom ratings and measurements of EEG amplitudes, and that the therapeutic effect would produce the most rapid improvements in early sessions of treatment. Method. "Blinded" research associates selected the first 100 patients from approximately 300 case files that met the following inclusion criteria: the person had received at least 10 treatment sessions, completed an initial CNS questionnaire, and that session-by-session subjective symptom ratings (SSRF) had been obtained. Patients ranged from 6 to 80 years old, almost evenly divided between male and female, with a wide range of symptoms and comorbid DSM-IV diagnoses. Results. Data were statistically analyzed for significance and corelational variables. Average symptom ratings across 15 major problem areas (e.g., anxiety, mood disturbance, attentional problems, fatigue, pain, sleep problems, etc.) showed significant improvements (p < .0001) from beginning to end of treatment. After an average of only 20 treatments the mean average of patient symptom ratings (0-10) declined from 7.92 to 3.96, a 50% improvement. Equally significant was the drop in EEG amplitude at the highest amplitude electrode site (HAS; p < .0001) as well as a lesser but still significant decrease at Cz (p < .002). A final analysis of the average symptom score with the HAS score showed them to be highly correlated. All hypotheses were confirmed. Conclusions. LENS treatment appears to be very efficient and effective in rapidly reducing a wide range of symptoms. It particularly produces rapid improvements in the first five to six sessions. Recommendations for future research are provided. Copyright © by The Haworth Press, Inc. All rights reserved.
View Full Paper →The value of quantitative electroencephalography in clinical psychiatry: a report by the Committee on Research of the American Neuropsychiatric Association
The authors evaluate quantitative electroencephalography (qEEG) as a laboratory test in clinical psychiatry and describe specific techniques, including visual analysis, spectral analysis, univariate comparisons to normative healthy databases, multivariate comparisons to normative healthy and clinical databases, and advanced techniques that hold clinical promise. Controversial aspects of each technique are discussed, as are broader areas of criticism, such as commercial interests and standards of evidence. The published literature is selectively reviewed, and qEEG's applicability is assessed for disorders of childhood (learning and attentional disorders), dementia, mood disorders, anxiety, panic, obsessive-compulsive disorder, and schizophrenia. Emphasis is placed primarily on studies that use qEEG to aid in clinical diagnosis, and secondarily on studies that use qEEG to predict medication response or clinical course. Methodological problems are highlighted, the availability of large databases is discussed, and specific recommendations are made for further research and development. As a clinical laboratory test, qEEG's cautious use is recommended in attentional and learning disabilities of childhood, and in mood and dementing disorders of adulthood.
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