- Email: [email protected]
Can Brain Rhythms Inform on Underlying Pathology in Schizophrenia?
Can Brain Rhythms Inform on Underlying Pathology in Schizophrenia? Miles A. Whittington
E
lectroencephalogram (EEG) rhythms represent an “at distance” measure of the temporal patterns of neuronal population activity. If a sufficient number of neurons in cortical tissue between recording electrodes receive the same repetitive synaptic input and/or generate the same repetitive sequence of outputs, then extracellular currents produced might manifest as a rhythmic field potential. But what can these rhythms tell us about the fundamental mechanisms of cortical function? Study in this area can be described as focused into two weakly overlapping groups: those that seek to identify the causal relationships between basic molecular and synaptic components of neuronal networks and their emergent population dynamics, and those that use EEG rhythms as a tool to permit quantification of changes in psychophysiological processes directly related to symptoms. The latter approach neatly side-steps the “devil’s advocate” view—that brain rhythms are epiphenomenal, merely representing the “sound of the engine running”— by following the analogy that a good mechanic can tell what is wrong with an engine just by listening to it. However, there is a tremendous opportunity to combine these two groups to provide accurate interpolation between identifiable mechanisms at the single cell/small network level up to observable deficits in motor, cognitive, and affective processes in patients. This opportunity is particularly attractive in schizophrenia research, where deficits are seen in certain rhythms and their accompanying cortical dynamics that are already well understood mechanistically. The EEG rhythms in the ␥ frequency range (30 – 80 Hz) have been directly implicated in cognitive processes since the pioneering work from Singer’s laboratories (1). The advent of in vitro models of ␥ frequency population rhythms has led to over 1 decade of research into their underlying mechanisms. In brief it is now accepted that ␥ rhythms are manifest through the influence of subpopulations of fast spiking interneurons on principal cells in massively parallel brain regions such as cortex. Excitation of these interneurons, whether it is tonic via ascending cortical input and/or neuromodulation or phasic via bursts of activity in principal neurons and more specifically their axons, is sufficient to induce trains of ␥-aminobutyric acid (GABA)A receptor-mediated inhibitory postsynaptic potentials at ␥ frequencies. These inhibitory potentials quantize the timing of outputs from principal cells, effectively constraining their action potential generation to narrow windows of opportunity recurring approximately every 25 msec. Feedback from principal cells, thus constrained, onto interneurons at both local and distal cortical sites provides a potent mechanism for imparting synchrony in cortical activity over relatively large distances. It is the identification of the specific type of interneuron involved in ␥ From the Institute of Neuroscience, The Medical School, Newcastle University, Newcastle, United Kingdom. Address reprint requests to Miles A. Whittington, B.Sc., Ph.D., Institute of Neuroscience, The Medical School, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK; E-mail: m.a.whittington@ ncl.ac.uk. Received February 5, 2008; revised February 11, 2008; accepted February 12, 2008.
0006-3223/08/$34.00 doi:10.1016/j.biopsych.2008.02.007
generation that has the potential to inform most about underlying mechanisms of cortical dysfunction in schizophrenia. The interneurons involved are predominantly although not exclusively (2) parvalbumin immunopositive perisomatic targeting cells. Deficits in immunocytochemical markers for these neurons and for GABAergic synaptic transmission itself are one of the most robust postmortem findings in brains from schizophrenic patients (3). The correlation between deficits in ␥ rhythm generation in schizophrenic patients (4) and loss of functional markers for parvalbumin immunopositive interneurons seems to be more than circumstantial. In a single brain region (superficial laminae of medial entorhinal cortex) both acute and genetic disruption of these interneurons directly correlate with reduced ␥ rhythmogenesis (Figure 1) (5). Exploring this mechanistic correlate further yields the possibility of combining multiple hypotheses for mechanisms underlying cortical dysfunction in schizophrenia further. A subset of interneurons critical for ␥ rhythmogenesis retains a large Nmethyl d-aspartate (NMDA) receptor-mediated drive into adulthood. In particular it has been shown that loss of parvalbumin immunopositive interneurons directly maps onto loss of the specific NMDA receptor subtype NR2A (6), and loss of parvalbumin signal is directly manipulable via this receptor in culture (7). These findings together suggest an intimate relationship between the GABA and NMDA theories for underlying pathology in the syndrome. But can it be that simple? Three papers in this issue demonstrate that caution is required in interpreting changes in NMDA/ interneuron function and changes in brain rhythms. Two factors that complicate the issue are elegantly evidenced in these works: firstly, it is clear that within the broad range of EEG rhythms recordable—and observed to be disrupted in schizophrenia— multiple frequency bands exist with, in some cases, starkly contrasting underlying mechanisms and cognitive relevance. Pinault et al. (8) demonstrate increases in ␥ power and a shift to higher frequencies in frontoparietal cortex after ketamine administration. Existing models of ␥ rhythmogenesis implicate NMDA receptor-dependent parvalbumin immunopositive interneurons only in rhythms at the lower end of the ␥ band (30 – 45 Hz). Mechanisms underlying higher frequencies within this band remain largely unexplored, but it is clear that functional separation exists when examining their relative roles in cognition (9). Extending the ␥ band lower into the 20 –30 Hz range (also called 2) also causes problems in interpretation, because cortical rhythms in this frequency range can be generated by entirely nonsynaptic mechanisms concurrently with conventional ␥ rhythms but in different cortical laminae (10). For example, Spencer et al. (11) directly compare phase locking at a range of frequencies with visual and auditory stimuli. Differences in visual responses were seen in the frequency range in which NMDAdependent parvalbumin immunopositive interneurons would be expected to play a role. However, the response to auditory stimulation occurred over a range of frequencies within which both conventional ␥ and 2 rhythms would be expected to contribute. In this case no changes were seen, but interaction between co-expressed rhythms is a complex and currently BIOL PSYCHIATRY 2008;63:728 –729 © 2008 Society of Biological Psychiatry
Commentary
BIOL PSYCHIATRY 2008;63:728 –729 729 and potentially rewarding one. Using models of brain disorder to probe mechanisms underlying that disorder can also be immensely revealing in terms of normal brain function. For example, a large cohort of work on mechanisms underlying epileptic neuronal activity in the 1970s and 1980s is directly responsible for shaping the understanding of many synaptic and intrinsic properties of neurons we take for granted today. The study of mechanisms underlying cortical dysfunction in schizophrenia—a brain disorder with a far more subtle electrographic signature— can build on this and lead to much deeper understanding of the processes involved in normal cognitive function.
Figure 1. N-methyl d-aspartate (NMDA) receptor blockade with ketamine generates the same laminar-specific deficits in ␥ rhythmogenesis as that seen in models with reduced parvalbumin immunopositive (PV⫹) cell numbers. Left panel shows an example of the distribution of PV⫹ cell bodies in medial entorhinal cortex (mEC) layers II–V. Middle panel shows the relative distribution of PV⫹ neurons, quantified within laminae, for control (wildtype) mice (white bars) and mice lacking the neurodevelopmental gene LPA1 (gray bars). Note significant decreases in PV⫹ neurons were only seen in LII. Right panel compares local field potential ␥ rhythm power in deep and superficial mEC layers in LPA1 ⫺/⫺ mice (gray symbols/line) and in normal mice in the presence of ketamine (black symbols/line). Note both models show a laminar-specific reduction in ␥ power in LII compared with control rhythms (white circles). Data from Cunningham et al. (5).
under-explored field. Coexistent  and ␥ rhythm-related dynamics might change in parallel in schizophrenia. For example, Ford et al. (12) show similar deficits in ␥ and  rhythms co-expressed as part of the same efference copy for motor function, and Uhlhaas et al. (13) demonstrated deficits in global synchrony in these two frequency bands in two different cognitive tasks. It is also possible for these two frequencies to interfere to some extent (10), making it difficult to interpret changes in one frequency band when more than one rhythm is co-expressed during the same task. Ford et al. also present data that they suggest might point to differential modulation of components of the efference copy from differing sensory modalities. This introduces the second major complicating factor in ascribing basic mechanisms to changes in EEG rhythms in patients: the brain seems to use different mechanisms of generation for identical frequencies of population rhythm in different cortical subregions. Spencer et al. (11) demonstrate that only the occipital and not the frontal component of the visual ␥-mediated phase locking is detrimentally affected in schizophrenia. Similarly, Pinault et al. show increases in fronto-parietal ␥ rhythms with acute application of ketamine, whereas decreases are seen in medial temporal structures (5,13). It is as yet unclear why the brain uses different strategies to generate near-identical rhythms; nor is it clear why the cortex uses so many different, discrete frequency bands to shape global activity patterns. However, this does make the subject a very rich
Dr. Whittington reported no biomedical financial interests or potential conflicts of interest. 1. Gray CM, König P, Engel AK, Singer W (1989): Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338:334 –337. 2. Gloveli T, Dugladze T, Saha S, Monyer H, Heinemann U, Traub RD, et al. (2005): Differential involvement of oriens/pyramidale interneurones in hippocampal network oscillations in vitro. J Physiol 562:131–147. 3. Lewis DA, Hashimoto T, Volk DW (2005): Cortical inhibitory neurons and schizophrenia. Nat Rev Neurosci 6:312–324. 4. Spencer KM, Nestor PG, Perlmutter R, Niznikiewicz MA, Klump MC, Frumin M, et al. (2004): Neural synchrony indexes disordered perception and cognition in schizophrenia. Proc Natl Acad Sci U S A 101:17288 – 17293. 5. Cunningham MO, Hunt J, Middleton S, LeBeau FE, Gillies MJ, Davies CH, et al. (2006): Region-specific reduction in entorhinal gamma oscillations and parvalbumin-immunoreactive neurons in animal models of psychiatric illness. J Neurosci 26:2767–2776. 6. Woo TU, Walsh JP, Benes FM (2004): Density of glutamic acid decarboxylase 67 messenger RNA-containing neurons that express the N-methyl-Daspartate receptor subunit NR2A in the anterior cingulate cortex in schizophrenia and bipolar disorder. Arch Gen Psychiatry 61:649 – 657. 7. Kinney JW, Davis CN, Tabarean I, Conti B, Bartfai T, Behrens MM (2006): A specific role for NR2A-containing NMDA receptors in the maintenance of parvalbumin and GAD67 immunoreactivity in cultured interneurons. J Neurosci 26:1604 –1615. 8. Pinault, D. Biol Psychiatry 63:730 –735. 9. Vidal JR, Chaumon M, O’Regan JK, Tallon-Baudry C (2006): Visual grouping and the focusing of attention induce gamma-band oscillations at different frequencies in human magnetoencephalogram signals. J Cogn Neurosci 18:1850 –1862. 10. Roopun AK, Middleton SJ, Cunningham MO, LeBeau FE, Bibbig A, Whittington MA, Traub RD (2006): A beta2-frequency (20 –30 Hz) oscillation in nonsynaptic networks of somatosensory cortex. Proc Natl Acad Sci U S A 103:15646 –15650. 11. Spencer KM, Niznikiewicz MA, Shenton ME, McCarley RW. Biol Psychiatry 63:744 –747. 12. Ford JM, Roach BJ, Faustman WO, Mathalon DH. Biol Psychiatry 63:736 – 743. 13. Uhlhaas PJ, Linden DE, Singer W, Haenschel C, Lindner M, Maurer K, Rodriguez E (2006): Dysfunctional long-range coordination of neural activity during Gestalt perception in schizophrenia. J Neurosci 26:8168 – 8175.
www.sobp.org/journal
E
lectroencephalogram (EEG) rhythms represent an “at distance” measure of the temporal patterns of neuronal population activity. If a sufficient number of neurons in cortical tissue between recording electrodes receive the same repetitive synaptic input and/or generate the same repetitive sequence of outputs, then extracellular currents produced might manifest as a rhythmic field potential. But what can these rhythms tell us about the fundamental mechanisms of cortical function? Study in this area can be described as focused into two weakly overlapping groups: those that seek to identify the causal relationships between basic molecular and synaptic components of neuronal networks and their emergent population dynamics, and those that use EEG rhythms as a tool to permit quantification of changes in psychophysiological processes directly related to symptoms. The latter approach neatly side-steps the “devil’s advocate” view—that brain rhythms are epiphenomenal, merely representing the “sound of the engine running”— by following the analogy that a good mechanic can tell what is wrong with an engine just by listening to it. However, there is a tremendous opportunity to combine these two groups to provide accurate interpolation between identifiable mechanisms at the single cell/small network level up to observable deficits in motor, cognitive, and affective processes in patients. This opportunity is particularly attractive in schizophrenia research, where deficits are seen in certain rhythms and their accompanying cortical dynamics that are already well understood mechanistically. The EEG rhythms in the ␥ frequency range (30 – 80 Hz) have been directly implicated in cognitive processes since the pioneering work from Singer’s laboratories (1). The advent of in vitro models of ␥ frequency population rhythms has led to over 1 decade of research into their underlying mechanisms. In brief it is now accepted that ␥ rhythms are manifest through the influence of subpopulations of fast spiking interneurons on principal cells in massively parallel brain regions such as cortex. Excitation of these interneurons, whether it is tonic via ascending cortical input and/or neuromodulation or phasic via bursts of activity in principal neurons and more specifically their axons, is sufficient to induce trains of ␥-aminobutyric acid (GABA)A receptor-mediated inhibitory postsynaptic potentials at ␥ frequencies. These inhibitory potentials quantize the timing of outputs from principal cells, effectively constraining their action potential generation to narrow windows of opportunity recurring approximately every 25 msec. Feedback from principal cells, thus constrained, onto interneurons at both local and distal cortical sites provides a potent mechanism for imparting synchrony in cortical activity over relatively large distances. It is the identification of the specific type of interneuron involved in ␥ From the Institute of Neuroscience, The Medical School, Newcastle University, Newcastle, United Kingdom. Address reprint requests to Miles A. Whittington, B.Sc., Ph.D., Institute of Neuroscience, The Medical School, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK; E-mail: m.a.whittington@ ncl.ac.uk. Received February 5, 2008; revised February 11, 2008; accepted February 12, 2008.
0006-3223/08/$34.00 doi:10.1016/j.biopsych.2008.02.007
generation that has the potential to inform most about underlying mechanisms of cortical dysfunction in schizophrenia. The interneurons involved are predominantly although not exclusively (2) parvalbumin immunopositive perisomatic targeting cells. Deficits in immunocytochemical markers for these neurons and for GABAergic synaptic transmission itself are one of the most robust postmortem findings in brains from schizophrenic patients (3). The correlation between deficits in ␥ rhythm generation in schizophrenic patients (4) and loss of functional markers for parvalbumin immunopositive interneurons seems to be more than circumstantial. In a single brain region (superficial laminae of medial entorhinal cortex) both acute and genetic disruption of these interneurons directly correlate with reduced ␥ rhythmogenesis (Figure 1) (5). Exploring this mechanistic correlate further yields the possibility of combining multiple hypotheses for mechanisms underlying cortical dysfunction in schizophrenia further. A subset of interneurons critical for ␥ rhythmogenesis retains a large Nmethyl d-aspartate (NMDA) receptor-mediated drive into adulthood. In particular it has been shown that loss of parvalbumin immunopositive interneurons directly maps onto loss of the specific NMDA receptor subtype NR2A (6), and loss of parvalbumin signal is directly manipulable via this receptor in culture (7). These findings together suggest an intimate relationship between the GABA and NMDA theories for underlying pathology in the syndrome. But can it be that simple? Three papers in this issue demonstrate that caution is required in interpreting changes in NMDA/ interneuron function and changes in brain rhythms. Two factors that complicate the issue are elegantly evidenced in these works: firstly, it is clear that within the broad range of EEG rhythms recordable—and observed to be disrupted in schizophrenia— multiple frequency bands exist with, in some cases, starkly contrasting underlying mechanisms and cognitive relevance. Pinault et al. (8) demonstrate increases in ␥ power and a shift to higher frequencies in frontoparietal cortex after ketamine administration. Existing models of ␥ rhythmogenesis implicate NMDA receptor-dependent parvalbumin immunopositive interneurons only in rhythms at the lower end of the ␥ band (30 – 45 Hz). Mechanisms underlying higher frequencies within this band remain largely unexplored, but it is clear that functional separation exists when examining their relative roles in cognition (9). Extending the ␥ band lower into the 20 –30 Hz range (also called 2) also causes problems in interpretation, because cortical rhythms in this frequency range can be generated by entirely nonsynaptic mechanisms concurrently with conventional ␥ rhythms but in different cortical laminae (10). For example, Spencer et al. (11) directly compare phase locking at a range of frequencies with visual and auditory stimuli. Differences in visual responses were seen in the frequency range in which NMDAdependent parvalbumin immunopositive interneurons would be expected to play a role. However, the response to auditory stimulation occurred over a range of frequencies within which both conventional ␥ and 2 rhythms would be expected to contribute. In this case no changes were seen, but interaction between co-expressed rhythms is a complex and currently BIOL PSYCHIATRY 2008;63:728 –729 © 2008 Society of Biological Psychiatry
Commentary
BIOL PSYCHIATRY 2008;63:728 –729 729 and potentially rewarding one. Using models of brain disorder to probe mechanisms underlying that disorder can also be immensely revealing in terms of normal brain function. For example, a large cohort of work on mechanisms underlying epileptic neuronal activity in the 1970s and 1980s is directly responsible for shaping the understanding of many synaptic and intrinsic properties of neurons we take for granted today. The study of mechanisms underlying cortical dysfunction in schizophrenia—a brain disorder with a far more subtle electrographic signature— can build on this and lead to much deeper understanding of the processes involved in normal cognitive function.
Figure 1. N-methyl d-aspartate (NMDA) receptor blockade with ketamine generates the same laminar-specific deficits in ␥ rhythmogenesis as that seen in models with reduced parvalbumin immunopositive (PV⫹) cell numbers. Left panel shows an example of the distribution of PV⫹ cell bodies in medial entorhinal cortex (mEC) layers II–V. Middle panel shows the relative distribution of PV⫹ neurons, quantified within laminae, for control (wildtype) mice (white bars) and mice lacking the neurodevelopmental gene LPA1 (gray bars). Note significant decreases in PV⫹ neurons were only seen in LII. Right panel compares local field potential ␥ rhythm power in deep and superficial mEC layers in LPA1 ⫺/⫺ mice (gray symbols/line) and in normal mice in the presence of ketamine (black symbols/line). Note both models show a laminar-specific reduction in ␥ power in LII compared with control rhythms (white circles). Data from Cunningham et al. (5).
under-explored field. Coexistent  and ␥ rhythm-related dynamics might change in parallel in schizophrenia. For example, Ford et al. (12) show similar deficits in ␥ and  rhythms co-expressed as part of the same efference copy for motor function, and Uhlhaas et al. (13) demonstrated deficits in global synchrony in these two frequency bands in two different cognitive tasks. It is also possible for these two frequencies to interfere to some extent (10), making it difficult to interpret changes in one frequency band when more than one rhythm is co-expressed during the same task. Ford et al. also present data that they suggest might point to differential modulation of components of the efference copy from differing sensory modalities. This introduces the second major complicating factor in ascribing basic mechanisms to changes in EEG rhythms in patients: the brain seems to use different mechanisms of generation for identical frequencies of population rhythm in different cortical subregions. Spencer et al. (11) demonstrate that only the occipital and not the frontal component of the visual ␥-mediated phase locking is detrimentally affected in schizophrenia. Similarly, Pinault et al. show increases in fronto-parietal ␥ rhythms with acute application of ketamine, whereas decreases are seen in medial temporal structures (5,13). It is as yet unclear why the brain uses different strategies to generate near-identical rhythms; nor is it clear why the cortex uses so many different, discrete frequency bands to shape global activity patterns. However, this does make the subject a very rich
Dr. Whittington reported no biomedical financial interests or potential conflicts of interest. 1. Gray CM, König P, Engel AK, Singer W (1989): Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338:334 –337. 2. Gloveli T, Dugladze T, Saha S, Monyer H, Heinemann U, Traub RD, et al. (2005): Differential involvement of oriens/pyramidale interneurones in hippocampal network oscillations in vitro. J Physiol 562:131–147. 3. Lewis DA, Hashimoto T, Volk DW (2005): Cortical inhibitory neurons and schizophrenia. Nat Rev Neurosci 6:312–324. 4. Spencer KM, Nestor PG, Perlmutter R, Niznikiewicz MA, Klump MC, Frumin M, et al. (2004): Neural synchrony indexes disordered perception and cognition in schizophrenia. Proc Natl Acad Sci U S A 101:17288 – 17293. 5. Cunningham MO, Hunt J, Middleton S, LeBeau FE, Gillies MJ, Davies CH, et al. (2006): Region-specific reduction in entorhinal gamma oscillations and parvalbumin-immunoreactive neurons in animal models of psychiatric illness. J Neurosci 26:2767–2776. 6. Woo TU, Walsh JP, Benes FM (2004): Density of glutamic acid decarboxylase 67 messenger RNA-containing neurons that express the N-methyl-Daspartate receptor subunit NR2A in the anterior cingulate cortex in schizophrenia and bipolar disorder. Arch Gen Psychiatry 61:649 – 657. 7. Kinney JW, Davis CN, Tabarean I, Conti B, Bartfai T, Behrens MM (2006): A specific role for NR2A-containing NMDA receptors in the maintenance of parvalbumin and GAD67 immunoreactivity in cultured interneurons. J Neurosci 26:1604 –1615. 8. Pinault, D. Biol Psychiatry 63:730 –735. 9. Vidal JR, Chaumon M, O’Regan JK, Tallon-Baudry C (2006): Visual grouping and the focusing of attention induce gamma-band oscillations at different frequencies in human magnetoencephalogram signals. J Cogn Neurosci 18:1850 –1862. 10. Roopun AK, Middleton SJ, Cunningham MO, LeBeau FE, Bibbig A, Whittington MA, Traub RD (2006): A beta2-frequency (20 –30 Hz) oscillation in nonsynaptic networks of somatosensory cortex. Proc Natl Acad Sci U S A 103:15646 –15650. 11. Spencer KM, Niznikiewicz MA, Shenton ME, McCarley RW. Biol Psychiatry 63:744 –747. 12. Ford JM, Roach BJ, Faustman WO, Mathalon DH. Biol Psychiatry 63:736 – 743. 13. Uhlhaas PJ, Linden DE, Singer W, Haenschel C, Lindner M, Maurer K, Rodriguez E (2006): Dysfunctional long-range coordination of neural activity during Gestalt perception in schizophrenia. J Neurosci 26:8168 – 8175.
www.sobp.org/journal