- Email: [email protected]
Neural Syntax in Mental Disorders
Commentary
Biological Psychiatry
Neural Syntax in Mental Disorders Brendon O. Watson and György Buzsáki It is difficult to parse the following sentence:
“Howlongdoesittakeforapracticingpsychiatristtodecipherthemeaningofthisnotsolongstringofwords?” because it lacks proper spacing and punctuation (the sentence should be: “How long does it take for a practicing psychiatrist to decipher the meaning of this not-so-long string of words?”). However, the brain can read a properly spaced sentence effortlessly. Segmentation of information is yet more important when the messages are coded in simple binary form, such as action potentials, the common currency of the brain. In addition, information in the brain is not simply conveyed between two neurons (a sender and a receiver) but by assemblies of neurons. Spiking activity must be coordinated across members of the assemblies, and a coding system (a “syntax”) must be in place for the transmitted messages to make sense to the downstream “reader” neurons. Syntactical rules allow for the generation of virtually infinite combinations from finite numbers of lexical elements. Syntax allows for the segmentation of information into a temporal sequence of discrete elements with ordered and hierarchical relationships resulting in clear interpretation of meaning. Temporal coordination in primary sensory areas has the luxury of synchronization to system-specific timing mechanisms, such as saccadic eye movements for vision or sniffing for olfaction. In deeper, more associative parts of the brain, such signals are not available, and these networks must generate their own timing signals in the form of oscillations. All known neuronal oscillations, or rhythms, are based on inhibition; the inhibitory phase of the oscillation provides a natural segmentation or “stop” signal for neuronal messages. “Chunking” of information into shorter and longer time-span patterns can be brought about by a system of nested brain rhythms, spanning several orders of magnitude in frequency range. These oscillations have a mathematically defined relationship to each other and form a strict hierarchy so that the phase of the slow oscillation modulates the power of the faster one, a phenomenon called cross-frequency coupling (1). By this simple solution, short messages contained in fast oscillations can be combined into longer ones by the slower rhythms. These heirarchically coupled messages can be thought of as neuronal letters, words, and sentences (2,3). Because neuronal oscillations are reflections of the temporally coordinated fluctuations of the transmembrane voltage in coherently active neurons, the aggregated sum is also recordable in the extracellular space as local field potential (4). Some rhythms are region-specific, whereas others are ubiquitous. Because temporal coordination of neuronal activity within and across brain regions is a prerequisite for cognition, the mesoscopic extracellular signals, in principle, can provide
precise and useful information about the healthy and pathologic regimens of network operations. However, to be useful as a “biomarker,” the mechanisms of the various oscillatory patterns need to be understood, their relationship with cognitive behaviors has to be revealed, and tools should be developed for their proper quantification (5). This special issue of Biological Psychiatry includes comprehensive reviews on the current state of the science of rhythmopathies or dysrhythmias (6) in psychiatric disease. As in any volume with multiple authors, complete coverage is not always achieved, but the multiple complementing themes addressed by the authors allow for selecting individual papers for a focused reading. Schizophrenia is a particular focus of the reviews in this issue. Schizophrenia is not just a cardinal psychiatric disease but is characterized by a mixture of “positive” delusions or hallucinations and more “negative” cognitive, mnemonic, and motivational deficits, which are much less treatable with current medications. It has been heavily studied using electrophysiologic tools including electroencephalography (EEG) and magnetoencephalography. Uhlhaas and Singer (7) present a comprehensive review of the known electrophysiologic disturbances in schizophrenia including gamma band changes especially in cognitively demanding tasks. They point to the possible relationship of excitatory-inhibitory balance in this gamma disturbance and some optogenetic studies in rodents supporting this hypothesis. They posit that these negative cognitive and perceptual changes may be more primary to schizophrenia, with the more classically associated positive symptoms resulting from adaptation to those more basic processing disturbances. Cognitive disturbances tend to be noticeable in patients with preschizophrenia earlier than active psychosis, and the authors propose the use of EEG and magnetoencephalography to study at-risk patients and eventually to diagnose schizophrenia. Senkowski and Gallinat (8) explain many links between behavior, task, and rhythm in patients with schizophrenia. Working memory and executive function are tasks of the prefrontal cortex and are disturbed in schizophrenia. Healthy subjects show increase in gamma power in the frontal pole during these tasks, whereas patients with schizophrenia show specific deficits among the same tasks inducing gamma-range power increases in the prefrontal cortex. Repetitive transcranial magnetic stimulation of dorsolateral prefrontal cortex may be a target for amelioration of these symptoms in these patients, a claim hinted at by some pharmacologic studies. Pittman-Polletta et al. (9) lay out a framework for understanding gamma, frontal, and theta activity all the way from molecules to rhythms to behavior. They link various molecular changes to oscillatory changes and discuss evidence showing that the same oscillations likely influence local microcircuit-level processing of information. Pathologies in
SEE COMMENTARY ON PAGE & 2015 Society of Biological Psychiatry Biological Psychiatry June 15, 2015; 77:998–1000 www.sobp.org/journal
998
http://dx.doi.org/10.1016/j.biopsych.2015.04.004 ISSN: 0006-3223
Biological Psychiatry
Commentary
these rhythms lead to poor coordination at the meso-scale, giving examples of theta disruptions leading to dysregulated hippocampal-prefrontal coordination and disruption of gamma modulation of basal ganglia rhythms in schizophrenia models. Finally, they connect these concepts to larger organism-scale cognitive dysfunction, showing correlations between gamma and beta rhythm changes and cognition and showing increased spontaneous gamma rhythmicity in patients actively experiencing hallucinations. Gonzalez-Burgos et al. (10) look deeply at a specific role for parvalbumin (PV)-positive GABAergic inhibitory interneurons (PV cells) in the gamma rhythm and cognitive changes in schizophrenia. Although total numbers of PV cells are normal in patients with schizophrenia, there is decreased GAD67 protein and messenger RNA in the cells of these patients in the dorsolateral prefrontal cortex, especially in basket cell axon terminals. The authors specify that it may be basket cell– type PV neurons rather than chandelier-type PV neurons that are primarily responsible for changes in schizophrenia and present evidence supporting both sides of the debate regarding whether the PV cell disturbance is primary or secondary/ compensatory to a primary excitatory glutamatergic cell disturbance. In a final look at schizophrenia, Rosen et al. (11) explore animal models of this complex disease. Although animal models are not expected to replicate human cognitive diseases, they can capture analogous features, which can be explored in greater detail. Animal models spanning from N-methyl-D-aspartate block–based pharmacologic preparations to various genetic models to neurodevelopmental models including maternal inflammation and neonatal hippocampal insult are discussed. The authors emphasize that animal models play a central role in efforts to link human symptoms and endophenotypes to underlying mechanisms. A second focus in this issue is major depressive disorder, which is a hugely morbid psychiatric disease throughout the world that affects roughly one in five Americans over the lifetime. Fingelkurts and Fingelkurts (12) summarize the fundamental EEG disturbances in major depression ranging from frontal alpha asymmetry, theta oscillation changes at the frontal pole, and unusual beta rhythm bouts. Alpha asymmetry has been a consistent finding over decades, and greater asymmetry is correlated with more severe depression and is relieved with effective treatment. In contrast, frontal midline theta band cordance has been most successful in distinguishing treatment-responsive from treatment-resistant patients and may relate to anterior cingulate cortex function. These authors frame major depression as a large-scale dyscoordination locally within brain regions and across brain regions and describe FactorD, an objective set of EEG measures that can characterize depressive symptoms with high sensitivity and specificity. Smart et al. (13) present work on the treatment of depression using neuromodulation therapies. They also summarize data correlating alpha asymmetry with depression severity and cure as well as theta cordance with treatment response, but they point out that these measures may not be specific enough to determine neuromodulation treatment decisions. They suggest that detailed measures of cross-frequency coupling combined with multimodal data, including functional
neuroimaging and tractography, may be a better approach for targeting symptoms with neuromodulation. Invasive, intracranial electrical recordings in postsurgical patients provide a much higher resolution picture than surface EEG and may be particularly informative in differentiating treatments and refining within-patient and cross-patient treatments and understanding of depression itself. The remaining articles look more generally at the role of oscillations in psychiatric disease and health. Cirelli and Tononi (14) take a unique approach to the electrophysiologic link between cortical development and sleep dynamics. They focus on rodent data showing major switch in many electrophysiologic measures at a time of critical behavioral and molecular changes, around 2 weeks of age in rodent pups. Immediately after birth, cortical EEG contains large silent periods punctuated by rhythmic oscillations that are localized rather than generalized. Soon after birth, long silences disappear and are replaced by coordinated delta waves in slow wave sleep, and beta band oscillations become synchronous across large swathes of cortex and are tightly linked to behavior. The maturation of this coordinated rhythm generation may be key to normal brain development. Fenton (15) introduces the term “discoordination” as a framework to link across psychiatric disease and animal models of disease. He carefully dissects the idea that neuropsychiatric disease may result from discoordination, from the idea of “cardinal cells” dedicated to specific function to the notion that misallocated neural resources secondary to poor rhythmicity may lead to aberrant recruitment of cells and assemblies. Quantifying and analyzing such discoordinated activity in animals can serve as grounds for hypothesis generation with subsequent testing in human patients. Voytek and Knight (16) point out a central role for oscillations in neural coordination as aligned with the various electrical disturbances seen across diseases. Oscillation abnormalities may lead to either overcoupling, as during the beta rhythm in patients with Parkinson’s disease, or undercoupling, as in patients with autism or schizophrenia where a decreased signal-to-noise ratio is noted. Such changes in signal-to-noise ratio may lead to misplacement of neuronal spikes into periods where there should be none. These authors also discuss the extent to which EEG and intracranial signals can be regarded as rhythmic or nonrhythmic. Resetting pathologic oscillatory activity using transcranial alternating current stimulation, repetitive transcranial magnetic stimulation, random noise stimulation, or other methods may allow correction of electrophysiologic disturbances found across a variety of diseases. Finally, Duan et al. (17) present novel work using optogenetically induced oscillations to perturb normal rat cognition. They use a virus to induce expression of excitatory opsins in the nucleus reuniens of rats and find that when they stimulate at delta frequency, rats display deficits during a working memory task. They link this work to working memory deficits in schizophrenia by showing that the N-methyl-D-aspartate receptor blocker ketamine induces delta waves in their animals quite similar to their stimulation. This study illustrates how new neuroscientific tools can deepen the connections between disease, symptom, endophenotype, and mechanism.
Biological Psychiatry June 15, 2015; 77:998–1000 www.sobp.org/journal
999
Biological Psychiatry
Commentary
In conclusion, the articles in this issue of Biological Psychiatry provide a testimony to the power of mesoscopic brain signals for understanding the nature of disease, assisting diagnosis, and quantifying the progress of therapy. Brain rhythms have been fully preserved throughout mammalian evolution and have constrained the evolutionary and ontogenetic scaling of brain structures (18). These mesoscopic signals are amenable for drug discovery because they often represent well-understood brain network–specific mechanisms. As markers of collective behavior of neuronal groups, they can be used as input signals for closed-loop therapies, using transcranial magnetic or electrical stimulation or other effectors in the feedback loop. With few psychoactive drugs on the horizon, these “electroceuticals” are expected to became part of the therapeutic landscape of psychiatry in the future. The success of such a venture depends largely on identifying selective and specific biomarkers of psychiatric symptoms.
Acknowledgments and Disclosures
3. 4. 5.
6.
7.
8.
9.
10.
11.
The authors report no biomedical financial interests or potential conflicts of interest.
12.
Article Information
13.
From the NYU Neuroscience Institute (BOW, GB), School of Medicine, and Center for Neural Science (GB), New York University; and Department of Psychiatry and Mind Brain Research Institute (BOW), Weill-Cornell Medical College, New York, New York. Address correspondence to Brendon O. Watson, M.D., Ph.D., NYU Neuroscience Institute, School of Medicine, 9th Floor, 450 East 29th Street, New York, NY 10016; E-mail: [email protected]. Received Apr 5, 2015; accepted Apr 7, 2015.
References 1. 2.
1000
Buzsáki G, Draguhn A (2004): Neuronal oscillations in cortical networks. Science 304:1926–1929. Buzsaki G (2006): Rhythms of the Brain. New York: Oxford University Press.
14.
15. 16.
17.
18.
Buzsáki G (2010): Neural syntax: Cell assemblies, synapsembles, and readers. Neuron 68:362–385. Buzsáki G, Da Silva FL (2012): High frequency oscillations in the intact brain. Prog Neurobiol 98:241–249. Buzsáki G, Watson BO (2012): Brain rhythms and neural syntax: Implications for efficient coding of cognitive content and neuropsychiatric disease. Dialogues Clin Neurosci 14:345–367. Schulman JJ, Cancro R, Lowe S, Lu F, Walton KD, Llinás RR (2011): Imaging of thalamocortical dysrhythmia in neuropsychiatry. Front Hum Neurosci 5:69. Uhlhaas PJ, Singer W (2015): Oscillations and neuronal dynamics in schizophrenia: The search for basic symptoms and translational opportunities. Biol Psychiatry 77:1001–1009. Senkowski D, Gallinat J (2015): Dysfunctional prefrontal gamma-band oscillations reflect working memory and other cognitive deficits in schizophrenia. Biol Psychiatry 77:1010–1019. Pittman-Polletta BR, Kocsis B, Vijayan S, Whittington MA, Kopell NJ (2015): Brain rhythms connect impaired inhibition to altered cognition in schizophrenia. Biol Psychiatry 77:1020–1030. Gonzalez-Burgos G, Cho RY, Lewis DA (2015): Alterations in cortical network oscillations and parvalbumin neurons in schizophrenia. Biol Psychiatry 77:1031–1040. Rosen AM, Spellman T, Gordon JA (2015): Electrophysiological endophenotypes in rodent models of schizophrenia and psychosis. Biol Psychiatry 77:1041–1049. Fingelkurts AA, Fingelkurts AA (2015): Altered structure of dynamic electroencephalogram oscillatory pattern in major depression. Biol Psychiatry 77:1050–1060. Smart OL, Tiruvadi VR, Mayberg HS (2015): Multimodal approaches to define network oscillations in depression. Biol Psychiatry 77:1061–1070. Cirelli C, Tononi G (2015): Cortical development, electroencephalogram rhythms, and the sleep/wake cycle. Biol Psychiatry 77: 1071–1078. Fenton AA (2015): Excitation-inhibition discoordination in rodent models of mental disorders. Biol Psychiatry 77:1079–1088. Voytek B, Knight RT (2015): Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease. Biol Psychiatry 77:1089–1097. Duan AR, Varela C, Zhang Y, Shen Y, Ziong L, Wilson M, Lisman J (2015): Delta frequency optogenetic stimulation of a thalamic nucleus reuniens is sufficient to produce working memory deficits; relevance to schizophrenia. Biol Psychiatry 77:1098–1107. Buzsáki G, Logothetis N, Singer W (2013): Scaling brain size, keeping timing: evolutionary preservation of brain rhythms. Neuron 80:751–764.
Biological Psychiatry June 15, 2015; 77:998–1000 www.sobp.org/journal
Biological Psychiatry
Neural Syntax in Mental Disorders Brendon O. Watson and György Buzsáki It is difficult to parse the following sentence:
“Howlongdoesittakeforapracticingpsychiatristtodecipherthemeaningofthisnotsolongstringofwords?” because it lacks proper spacing and punctuation (the sentence should be: “How long does it take for a practicing psychiatrist to decipher the meaning of this not-so-long string of words?”). However, the brain can read a properly spaced sentence effortlessly. Segmentation of information is yet more important when the messages are coded in simple binary form, such as action potentials, the common currency of the brain. In addition, information in the brain is not simply conveyed between two neurons (a sender and a receiver) but by assemblies of neurons. Spiking activity must be coordinated across members of the assemblies, and a coding system (a “syntax”) must be in place for the transmitted messages to make sense to the downstream “reader” neurons. Syntactical rules allow for the generation of virtually infinite combinations from finite numbers of lexical elements. Syntax allows for the segmentation of information into a temporal sequence of discrete elements with ordered and hierarchical relationships resulting in clear interpretation of meaning. Temporal coordination in primary sensory areas has the luxury of synchronization to system-specific timing mechanisms, such as saccadic eye movements for vision or sniffing for olfaction. In deeper, more associative parts of the brain, such signals are not available, and these networks must generate their own timing signals in the form of oscillations. All known neuronal oscillations, or rhythms, are based on inhibition; the inhibitory phase of the oscillation provides a natural segmentation or “stop” signal for neuronal messages. “Chunking” of information into shorter and longer time-span patterns can be brought about by a system of nested brain rhythms, spanning several orders of magnitude in frequency range. These oscillations have a mathematically defined relationship to each other and form a strict hierarchy so that the phase of the slow oscillation modulates the power of the faster one, a phenomenon called cross-frequency coupling (1). By this simple solution, short messages contained in fast oscillations can be combined into longer ones by the slower rhythms. These heirarchically coupled messages can be thought of as neuronal letters, words, and sentences (2,3). Because neuronal oscillations are reflections of the temporally coordinated fluctuations of the transmembrane voltage in coherently active neurons, the aggregated sum is also recordable in the extracellular space as local field potential (4). Some rhythms are region-specific, whereas others are ubiquitous. Because temporal coordination of neuronal activity within and across brain regions is a prerequisite for cognition, the mesoscopic extracellular signals, in principle, can provide
precise and useful information about the healthy and pathologic regimens of network operations. However, to be useful as a “biomarker,” the mechanisms of the various oscillatory patterns need to be understood, their relationship with cognitive behaviors has to be revealed, and tools should be developed for their proper quantification (5). This special issue of Biological Psychiatry includes comprehensive reviews on the current state of the science of rhythmopathies or dysrhythmias (6) in psychiatric disease. As in any volume with multiple authors, complete coverage is not always achieved, but the multiple complementing themes addressed by the authors allow for selecting individual papers for a focused reading. Schizophrenia is a particular focus of the reviews in this issue. Schizophrenia is not just a cardinal psychiatric disease but is characterized by a mixture of “positive” delusions or hallucinations and more “negative” cognitive, mnemonic, and motivational deficits, which are much less treatable with current medications. It has been heavily studied using electrophysiologic tools including electroencephalography (EEG) and magnetoencephalography. Uhlhaas and Singer (7) present a comprehensive review of the known electrophysiologic disturbances in schizophrenia including gamma band changes especially in cognitively demanding tasks. They point to the possible relationship of excitatory-inhibitory balance in this gamma disturbance and some optogenetic studies in rodents supporting this hypothesis. They posit that these negative cognitive and perceptual changes may be more primary to schizophrenia, with the more classically associated positive symptoms resulting from adaptation to those more basic processing disturbances. Cognitive disturbances tend to be noticeable in patients with preschizophrenia earlier than active psychosis, and the authors propose the use of EEG and magnetoencephalography to study at-risk patients and eventually to diagnose schizophrenia. Senkowski and Gallinat (8) explain many links between behavior, task, and rhythm in patients with schizophrenia. Working memory and executive function are tasks of the prefrontal cortex and are disturbed in schizophrenia. Healthy subjects show increase in gamma power in the frontal pole during these tasks, whereas patients with schizophrenia show specific deficits among the same tasks inducing gamma-range power increases in the prefrontal cortex. Repetitive transcranial magnetic stimulation of dorsolateral prefrontal cortex may be a target for amelioration of these symptoms in these patients, a claim hinted at by some pharmacologic studies. Pittman-Polletta et al. (9) lay out a framework for understanding gamma, frontal, and theta activity all the way from molecules to rhythms to behavior. They link various molecular changes to oscillatory changes and discuss evidence showing that the same oscillations likely influence local microcircuit-level processing of information. Pathologies in
SEE COMMENTARY ON PAGE & 2015 Society of Biological Psychiatry Biological Psychiatry June 15, 2015; 77:998–1000 www.sobp.org/journal
998
http://dx.doi.org/10.1016/j.biopsych.2015.04.004 ISSN: 0006-3223
Biological Psychiatry
Commentary
these rhythms lead to poor coordination at the meso-scale, giving examples of theta disruptions leading to dysregulated hippocampal-prefrontal coordination and disruption of gamma modulation of basal ganglia rhythms in schizophrenia models. Finally, they connect these concepts to larger organism-scale cognitive dysfunction, showing correlations between gamma and beta rhythm changes and cognition and showing increased spontaneous gamma rhythmicity in patients actively experiencing hallucinations. Gonzalez-Burgos et al. (10) look deeply at a specific role for parvalbumin (PV)-positive GABAergic inhibitory interneurons (PV cells) in the gamma rhythm and cognitive changes in schizophrenia. Although total numbers of PV cells are normal in patients with schizophrenia, there is decreased GAD67 protein and messenger RNA in the cells of these patients in the dorsolateral prefrontal cortex, especially in basket cell axon terminals. The authors specify that it may be basket cell– type PV neurons rather than chandelier-type PV neurons that are primarily responsible for changes in schizophrenia and present evidence supporting both sides of the debate regarding whether the PV cell disturbance is primary or secondary/ compensatory to a primary excitatory glutamatergic cell disturbance. In a final look at schizophrenia, Rosen et al. (11) explore animal models of this complex disease. Although animal models are not expected to replicate human cognitive diseases, they can capture analogous features, which can be explored in greater detail. Animal models spanning from N-methyl-D-aspartate block–based pharmacologic preparations to various genetic models to neurodevelopmental models including maternal inflammation and neonatal hippocampal insult are discussed. The authors emphasize that animal models play a central role in efforts to link human symptoms and endophenotypes to underlying mechanisms. A second focus in this issue is major depressive disorder, which is a hugely morbid psychiatric disease throughout the world that affects roughly one in five Americans over the lifetime. Fingelkurts and Fingelkurts (12) summarize the fundamental EEG disturbances in major depression ranging from frontal alpha asymmetry, theta oscillation changes at the frontal pole, and unusual beta rhythm bouts. Alpha asymmetry has been a consistent finding over decades, and greater asymmetry is correlated with more severe depression and is relieved with effective treatment. In contrast, frontal midline theta band cordance has been most successful in distinguishing treatment-responsive from treatment-resistant patients and may relate to anterior cingulate cortex function. These authors frame major depression as a large-scale dyscoordination locally within brain regions and across brain regions and describe FactorD, an objective set of EEG measures that can characterize depressive symptoms with high sensitivity and specificity. Smart et al. (13) present work on the treatment of depression using neuromodulation therapies. They also summarize data correlating alpha asymmetry with depression severity and cure as well as theta cordance with treatment response, but they point out that these measures may not be specific enough to determine neuromodulation treatment decisions. They suggest that detailed measures of cross-frequency coupling combined with multimodal data, including functional
neuroimaging and tractography, may be a better approach for targeting symptoms with neuromodulation. Invasive, intracranial electrical recordings in postsurgical patients provide a much higher resolution picture than surface EEG and may be particularly informative in differentiating treatments and refining within-patient and cross-patient treatments and understanding of depression itself. The remaining articles look more generally at the role of oscillations in psychiatric disease and health. Cirelli and Tononi (14) take a unique approach to the electrophysiologic link between cortical development and sleep dynamics. They focus on rodent data showing major switch in many electrophysiologic measures at a time of critical behavioral and molecular changes, around 2 weeks of age in rodent pups. Immediately after birth, cortical EEG contains large silent periods punctuated by rhythmic oscillations that are localized rather than generalized. Soon after birth, long silences disappear and are replaced by coordinated delta waves in slow wave sleep, and beta band oscillations become synchronous across large swathes of cortex and are tightly linked to behavior. The maturation of this coordinated rhythm generation may be key to normal brain development. Fenton (15) introduces the term “discoordination” as a framework to link across psychiatric disease and animal models of disease. He carefully dissects the idea that neuropsychiatric disease may result from discoordination, from the idea of “cardinal cells” dedicated to specific function to the notion that misallocated neural resources secondary to poor rhythmicity may lead to aberrant recruitment of cells and assemblies. Quantifying and analyzing such discoordinated activity in animals can serve as grounds for hypothesis generation with subsequent testing in human patients. Voytek and Knight (16) point out a central role for oscillations in neural coordination as aligned with the various electrical disturbances seen across diseases. Oscillation abnormalities may lead to either overcoupling, as during the beta rhythm in patients with Parkinson’s disease, or undercoupling, as in patients with autism or schizophrenia where a decreased signal-to-noise ratio is noted. Such changes in signal-to-noise ratio may lead to misplacement of neuronal spikes into periods where there should be none. These authors also discuss the extent to which EEG and intracranial signals can be regarded as rhythmic or nonrhythmic. Resetting pathologic oscillatory activity using transcranial alternating current stimulation, repetitive transcranial magnetic stimulation, random noise stimulation, or other methods may allow correction of electrophysiologic disturbances found across a variety of diseases. Finally, Duan et al. (17) present novel work using optogenetically induced oscillations to perturb normal rat cognition. They use a virus to induce expression of excitatory opsins in the nucleus reuniens of rats and find that when they stimulate at delta frequency, rats display deficits during a working memory task. They link this work to working memory deficits in schizophrenia by showing that the N-methyl-D-aspartate receptor blocker ketamine induces delta waves in their animals quite similar to their stimulation. This study illustrates how new neuroscientific tools can deepen the connections between disease, symptom, endophenotype, and mechanism.
Biological Psychiatry June 15, 2015; 77:998–1000 www.sobp.org/journal
999
Biological Psychiatry
Commentary
In conclusion, the articles in this issue of Biological Psychiatry provide a testimony to the power of mesoscopic brain signals for understanding the nature of disease, assisting diagnosis, and quantifying the progress of therapy. Brain rhythms have been fully preserved throughout mammalian evolution and have constrained the evolutionary and ontogenetic scaling of brain structures (18). These mesoscopic signals are amenable for drug discovery because they often represent well-understood brain network–specific mechanisms. As markers of collective behavior of neuronal groups, they can be used as input signals for closed-loop therapies, using transcranial magnetic or electrical stimulation or other effectors in the feedback loop. With few psychoactive drugs on the horizon, these “electroceuticals” are expected to became part of the therapeutic landscape of psychiatry in the future. The success of such a venture depends largely on identifying selective and specific biomarkers of psychiatric symptoms.
Acknowledgments and Disclosures
3. 4. 5.
6.
7.
8.
9.
10.
11.
The authors report no biomedical financial interests or potential conflicts of interest.
12.
Article Information
13.
From the NYU Neuroscience Institute (BOW, GB), School of Medicine, and Center for Neural Science (GB), New York University; and Department of Psychiatry and Mind Brain Research Institute (BOW), Weill-Cornell Medical College, New York, New York. Address correspondence to Brendon O. Watson, M.D., Ph.D., NYU Neuroscience Institute, School of Medicine, 9th Floor, 450 East 29th Street, New York, NY 10016; E-mail: [email protected]. Received Apr 5, 2015; accepted Apr 7, 2015.
References 1. 2.
1000
Buzsáki G, Draguhn A (2004): Neuronal oscillations in cortical networks. Science 304:1926–1929. Buzsaki G (2006): Rhythms of the Brain. New York: Oxford University Press.
14.
15. 16.
17.
18.
Buzsáki G (2010): Neural syntax: Cell assemblies, synapsembles, and readers. Neuron 68:362–385. Buzsáki G, Da Silva FL (2012): High frequency oscillations in the intact brain. Prog Neurobiol 98:241–249. Buzsáki G, Watson BO (2012): Brain rhythms and neural syntax: Implications for efficient coding of cognitive content and neuropsychiatric disease. Dialogues Clin Neurosci 14:345–367. Schulman JJ, Cancro R, Lowe S, Lu F, Walton KD, Llinás RR (2011): Imaging of thalamocortical dysrhythmia in neuropsychiatry. Front Hum Neurosci 5:69. Uhlhaas PJ, Singer W (2015): Oscillations and neuronal dynamics in schizophrenia: The search for basic symptoms and translational opportunities. Biol Psychiatry 77:1001–1009. Senkowski D, Gallinat J (2015): Dysfunctional prefrontal gamma-band oscillations reflect working memory and other cognitive deficits in schizophrenia. Biol Psychiatry 77:1010–1019. Pittman-Polletta BR, Kocsis B, Vijayan S, Whittington MA, Kopell NJ (2015): Brain rhythms connect impaired inhibition to altered cognition in schizophrenia. Biol Psychiatry 77:1020–1030. Gonzalez-Burgos G, Cho RY, Lewis DA (2015): Alterations in cortical network oscillations and parvalbumin neurons in schizophrenia. Biol Psychiatry 77:1031–1040. Rosen AM, Spellman T, Gordon JA (2015): Electrophysiological endophenotypes in rodent models of schizophrenia and psychosis. Biol Psychiatry 77:1041–1049. Fingelkurts AA, Fingelkurts AA (2015): Altered structure of dynamic electroencephalogram oscillatory pattern in major depression. Biol Psychiatry 77:1050–1060. Smart OL, Tiruvadi VR, Mayberg HS (2015): Multimodal approaches to define network oscillations in depression. Biol Psychiatry 77:1061–1070. Cirelli C, Tononi G (2015): Cortical development, electroencephalogram rhythms, and the sleep/wake cycle. Biol Psychiatry 77: 1071–1078. Fenton AA (2015): Excitation-inhibition discoordination in rodent models of mental disorders. Biol Psychiatry 77:1079–1088. Voytek B, Knight RT (2015): Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease. Biol Psychiatry 77:1089–1097. Duan AR, Varela C, Zhang Y, Shen Y, Ziong L, Wilson M, Lisman J (2015): Delta frequency optogenetic stimulation of a thalamic nucleus reuniens is sufficient to produce working memory deficits; relevance to schizophrenia. Biol Psychiatry 77:1098–1107. Buzsáki G, Logothetis N, Singer W (2013): Scaling brain size, keeping timing: evolutionary preservation of brain rhythms. Neuron 80:751–764.
Biological Psychiatry June 15, 2015; 77:998–1000 www.sobp.org/journal