- Email: [email protected]
Functional connectivity in auditory cortex using chronic, multichannel unit recordings
Neurocomputing 26}27 (1999) 347}354
Functional connectivity in auditory cortex using chronic, multichannel unit recordings Ryan S. Clement, Russell S. Witte, Patrick J. Rousche, Daryl R. Kipke* Bioengineering Program, Arizona State University, Tempe, AZ 85287-6006, USA Accepted 18 December 1998
Abstract Chronic, multichannel recordings provide a method for reliable detection and determination of long-term dynamic functional connectivity. Using chronically implanted multichannel electrode arrays, we simultaneously recorded 30}70 units in guinea pig auditory cortex in daily recording sessions for implant durations of six months. We examined stimulus response properties and correlation strengths in neuron pairs in four animals. Preliminary results from these &snapshots' of functional connectivity suggest sparse functional connections among widely distributed neurons. Some of these functional connections were found to persist for several days. These results provide a framework upon which further investigations of functional dynamic connectivity can be developed. 1999 Published by Elsevier Science B.V. All rights reserved. Keywords: Auditory cortex; Correlation; Plasticity; Connectivity
1. Introduction The auditory cortex is a dynamic structure on many time scales. On a scale of seconds to minutes, functional connectivity between two neurons may change as a function of auditory stimulation [2,4] or behavior [1]. On a scale of days to weeks, the frequency map may reorganize under certain behavioral conditions [7]. Within this context, it is reasonable to hypothesize that functional connectivity, although dynamic in the short-term, may exhibit long-term trends that underlie cortical plasticity. To address this general hypothesis requires investigation in two temporal domains: (1) &Snapshot' measures (10}20 min duration) of the connectivity `statea of * Corresponding author. E-mail: [email protected]. 0925-2312/99/$ } see front matter 1999 Published by Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 2 3 1 2 ( 9 9 ) 0 0 0 2 3 - 5
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R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354
auditory cortex describe the range of possible functional connectivity; (2) Day-to-day comparisons of &snapshots' determine dynamic functional connectivity over time. Correlation and synchrony analysis provide a starting point for evaluating individual &snapshots' for this investigation, but only by chronic recordings using microelectrode arrays in auditory cortex of awake and behaving animals will assessment of dynamic functional connectivity be possible [3]. In this paper, we describe an experimental technique for investigating dynamic functional connectivity over short and long durations in auditory cortex and report some initial "ndings. In a 15 min temporal snapshot, functional connectivity is relatively sparse within ensembles consisting of over 30 simultaneously recorded neurons. Although the detected set of correlations are highly variable and generally stimulus related, the most common feature is an excitatory central mound that is not strongly related to inter-neuron distance. In a three-day comparison of 15 min snapshots of a pair of nearby units, we show that near-coincidence excitatory correlations persist remarkably well over this time.
2. Methods Neural recording and auditory stimulation: Each animal received an unilateral implant of a 33-channel, microelectrode array in auditory cortex. Details of data collection and recording procedures are described elsewhere [10]. Brie#y, single units and multiunit clusters, as determined by threshold crossing and template matching alogrithms, were recorded simultaneously for all electrodes (Plexon, Inc., Dallas, TX). Auditory stimulus generation was controlled using a computer-based sound system (Tucker-Davis Technologies Inc., Gainesville, FL). A routine testing set of various pure tones, broad-band noise stimuli or frequency sweeps were used to classify each recorded unit. In addition, unit responses were recorded for 10 min of silence each session. All auditory stimulation was delivered free-"eld within an enclosed soundattenuated chamber through a single high-performance speaker placed directly above the animal's head. Data analysis: Standard neurophysiological measures of neural activity were computed o!-line using custom-developed software. Auto-correlograms, interval histograms, and peri-event stimulus histograms were determined for only well-de"ned single units (typically those above 80 lV). Cross-correlograms were determined among pairs of the chosen units for each session. Cross-correlograms were normalized for shared-stimulus driving using the PST-predictor [6], and correlation strength was assessed as the probability of the peak correlation relative to mean probability of the uncorrelated background.
3. Results A series of four animals have been implanted in our experimental series. We report a small amount of results which are representative of the general "ndings regarding
R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354
349
Fig. 1. (A) Oscilloscope traces of typical extracellular potentials from one electrode. (B) Spike raster plot for simultaneously recorded neural ensemble during a normal recording session. A 20 s portion of a 15 min spontaneous recording is shown (g18:101298sp1).
long-term connectivity properties. The typical daily recordings provided from 30}70 single unit and multiunit clusters distributed across auditory cortex (Fig. 1) and were used to generate snapshots of daily connectivity. Snapshots spanning three consecutive recordings days were also obtained. 3.1. Snapshots demonstrate sparse functional connectivity Simultaneously recorded units in auditory cortex show sparse connectivity under both spontaneous conditions and under stimulation from simple auditory stimuli. We can estimate a connectivity &snapshot' in an individual animal by computing the "rst-order cross-correlations among the possible unit pairs. A sample of these crosscorrelograms, during a 10 min silent/non-stimulated period referenced to one unit
350
R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354
Fig. 2. Correlated activity distributed across the electrode array under spontaneous conditions in the cortex of a guinea pig. The same reference (unit 7a) is chosen for all of the correlation pairs. Crosscorrelograms are expressed in conditional spike probability. Bin width"10 ms; Recording duration of &15 min.
near the corner of the array illustrates the general "ndings (Fig. 2). Pairwise correlation, where present, consists of a positive peak near or surrounding the zero-latency point in the cross-correlogram (central mound). From this data set, we did not detect a distance e!ect on the correlation pattern across the neural ensemble. With auditory stimulation, correlations were found for units with both similar and dissimilar stimulus responses.
R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354
351
3.2. Snapshots compared from day-to-day show persistence of functional connectivity To investigate dynamic patterns of functional connectivity over successive recording days, we compare snapshots from each day for a single electrode. In Fig. 3c, we show that functional connectivity for two units on the same electrode persists over a three-day period. Note the near-coincident positive correlation (latency &1 ms) and unchanging correlation strength (peak conditional spike probability remains at &0.004) (Fig. 3c). Although these two units maintained consistent interval histograms (see 3a), the stimulus-driven PSTHs show variation (3b).
4. Discussion Chronic, multichannel recordings provide the only method for reliable detection and determination of long-term dynamic functional correlations within the auditory cortex. We have examined stimulus response properties and correlation strengths in best-neuron pairs in four animals. Our preliminary results from &snapshots' of functional connectivity in these subjects suggest a sparse connectivity between any given neuron pairs using inter-electrode spacing ranging from 250 to 2500 lm. However, despite this sparseness, we demonstrate that units displaying functional connectivity in a single snapshot can maintain that representation for up to three days. These results provide a framework upon which further investigations of functional dynamic connectivity can be developed. 4.1. Why is functional connectivity so sparse? Previous results from anaesthetized animals have suggested that signi"cant pairwise correlations between randomly chosen neurons in auditory cortex of cat occur with probability of &50% [3]. In our awake, but restrained guinea pigs, we found incidences of correlations in less than 10% of pairs. These discrepancies could suggest that animals in an awake state maintain more variable and dynamic connection patterns. Alternatively, our low correlation probabilities may relate to the use of less than optimal simple stimuli [2]. 4.2. What can dynamic functional correlation analysis reveal about cortical processing? In developing a chronic preparation capable of discriminating the same units over extended time periods [10], we have begun to investigate issues relating to dynamic functional correlation in simultaneously recorded neural ensembles. Such analysis meshes well with theories regarding neuronal population encoding such as the dynamic neuronal assemblies postulated by Vaadia et al. and Gerstein or the synchronized events suggested by Riehle et al. [5,8,9]. Our chronic approach allows for the determination of a hardwired framework upon which neuronal assemblies could be created or modi"ed. The sparseness of correlations however, suggests that alternative
352
R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354
Fig. 3. An example of correlation during noise bursts on an electrode that persists for three consecutive days. Row A contains the IHs for both units for each day. Row B shows the PSTHs (bin width"1 ms). Row C contains both the normalized cross-correlations (scaled in probability of a spike), and the averaged waveforms of the units for each day (bin width"0.25 ms).
R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354
353
methods may be required to fully investigate functional dynamic correlations among many neurons. Cortical mechanisms such as plasticity, learning or memory may best be described in terms of dynamic correlations.
References [1] E. Ahissar, E. Vaadia, M. Ahissar, H. Bergman, A. Arieli, M. Abeles, Dependence of cortical plasticity on correlated activity of single neurons and on behavioral context, Science 257 (1992) 1412}1415. [2] R.C. deCharms, D.T. Blake, M.M. Merzenich, Optimizing sound features for cortical neurons [see comments], Science 280 (1998) 1439}1443. [3] J.J. Eggermont, G.M. Smith, Neural connectivity only accounts for a small part of neural correlation in auditory cortex, Exp. Brain Res. 110 (1996) 379}391. [4] R.D. Frostig, Y. Gottlieb, E. Vaadia, M. Abeles, The e!ects of stimuli on the activity and functional connectivity of local neuronal groups in the cat auditory cortex, Brain Res. 272 (1983) 211}221. [5] G.L. Gerstein, P. Bedenbaugh, A.M.H.J. Aertsen, Neuronal assemblies, IEEE Trans. Biomed. Eng. 36 (1989) 4}14. [6] G. Palm, A.M.H.J. Aertsen, G.L. Gerstein, On the signi"cance of correlations among neuronal spike trains, Biol. Cybernet. 59 (1988) 1}11. [7] G.H. Recanzone, C.E. Schreiner, M.M. Merzenich, Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys, J. Neuroscience 13 (1) (1993) 87}103. [8] A. Riehle, S. Grun, M. Diesmann, A. Aertsen, Spike synchronization and rate modulation di!erentially involved in motor cortical function [see comments], Science 278 (1997) 1950}1953. [9] E. Vaadia, H. Bergman, M. Abeles, Neuronal activities related to higher brain functions } theoretical and experimental implications, IEEE Trans. Biomed. Eng. 36 (1989) 25}35. [10] J.C. Williams, R.L. Rennaker, D.R. Kipke, Stability of chronic multichannel neural recordings: implications for a long-term neural interface, Neurocomputing (1999), this volume.
R.S. Clement received a B.S. in Electrical Engineering from Ohio Northern University in May 1996. He is currently a graduate student at Arizona State University in the Bioengineering Program. His graduate work explores topics related to auditory processing in the cortex, and new studies in the cortical neural responses to cochlear implants. His interests include neuroprosthetics, hearing, and neuroscience.
R.S. Witte received a B.S. in Physics from the University of Arizona in August 1993. He is currently a graduate student at Arizona State University. His doctoral thesis explores the dynamics of cortical plasticity in auditory cortex of behaving animals. His major interests include "ber optics, neural prostheses, and evolving sensory processes of the brain.
354
R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354 P.J. Rousche received a Ph.D. degree in Bioengineering from the University of Utah in 1996. He is currently a faculty associate in Bioengineering at Arizona State University. His research interests include the collection and analysis of multiple channel electrode data in sensory cortex for the investigation of dynamic cortical processes such as plasticity, learning and memory, and the development of a neural prostheses.
D.R. Kipke received a Ph.D. degree in Bioengineering from the University of Michigan in 1991. He is currently an Associate Professor in Bioengineering at Arizona State University. His research involves auditory processing, neural prostheses, cortical plasticity, and neural implant technologies.
Functional connectivity in auditory cortex using chronic, multichannel unit recordings Ryan S. Clement, Russell S. Witte, Patrick J. Rousche, Daryl R. Kipke* Bioengineering Program, Arizona State University, Tempe, AZ 85287-6006, USA Accepted 18 December 1998
Abstract Chronic, multichannel recordings provide a method for reliable detection and determination of long-term dynamic functional connectivity. Using chronically implanted multichannel electrode arrays, we simultaneously recorded 30}70 units in guinea pig auditory cortex in daily recording sessions for implant durations of six months. We examined stimulus response properties and correlation strengths in neuron pairs in four animals. Preliminary results from these &snapshots' of functional connectivity suggest sparse functional connections among widely distributed neurons. Some of these functional connections were found to persist for several days. These results provide a framework upon which further investigations of functional dynamic connectivity can be developed. 1999 Published by Elsevier Science B.V. All rights reserved. Keywords: Auditory cortex; Correlation; Plasticity; Connectivity
1. Introduction The auditory cortex is a dynamic structure on many time scales. On a scale of seconds to minutes, functional connectivity between two neurons may change as a function of auditory stimulation [2,4] or behavior [1]. On a scale of days to weeks, the frequency map may reorganize under certain behavioral conditions [7]. Within this context, it is reasonable to hypothesize that functional connectivity, although dynamic in the short-term, may exhibit long-term trends that underlie cortical plasticity. To address this general hypothesis requires investigation in two temporal domains: (1) &Snapshot' measures (10}20 min duration) of the connectivity `statea of * Corresponding author. E-mail: [email protected]. 0925-2312/99/$ } see front matter 1999 Published by Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 2 3 1 2 ( 9 9 ) 0 0 0 2 3 - 5
348
R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354
auditory cortex describe the range of possible functional connectivity; (2) Day-to-day comparisons of &snapshots' determine dynamic functional connectivity over time. Correlation and synchrony analysis provide a starting point for evaluating individual &snapshots' for this investigation, but only by chronic recordings using microelectrode arrays in auditory cortex of awake and behaving animals will assessment of dynamic functional connectivity be possible [3]. In this paper, we describe an experimental technique for investigating dynamic functional connectivity over short and long durations in auditory cortex and report some initial "ndings. In a 15 min temporal snapshot, functional connectivity is relatively sparse within ensembles consisting of over 30 simultaneously recorded neurons. Although the detected set of correlations are highly variable and generally stimulus related, the most common feature is an excitatory central mound that is not strongly related to inter-neuron distance. In a three-day comparison of 15 min snapshots of a pair of nearby units, we show that near-coincidence excitatory correlations persist remarkably well over this time.
2. Methods Neural recording and auditory stimulation: Each animal received an unilateral implant of a 33-channel, microelectrode array in auditory cortex. Details of data collection and recording procedures are described elsewhere [10]. Brie#y, single units and multiunit clusters, as determined by threshold crossing and template matching alogrithms, were recorded simultaneously for all electrodes (Plexon, Inc., Dallas, TX). Auditory stimulus generation was controlled using a computer-based sound system (Tucker-Davis Technologies Inc., Gainesville, FL). A routine testing set of various pure tones, broad-band noise stimuli or frequency sweeps were used to classify each recorded unit. In addition, unit responses were recorded for 10 min of silence each session. All auditory stimulation was delivered free-"eld within an enclosed soundattenuated chamber through a single high-performance speaker placed directly above the animal's head. Data analysis: Standard neurophysiological measures of neural activity were computed o!-line using custom-developed software. Auto-correlograms, interval histograms, and peri-event stimulus histograms were determined for only well-de"ned single units (typically those above 80 lV). Cross-correlograms were determined among pairs of the chosen units for each session. Cross-correlograms were normalized for shared-stimulus driving using the PST-predictor [6], and correlation strength was assessed as the probability of the peak correlation relative to mean probability of the uncorrelated background.
3. Results A series of four animals have been implanted in our experimental series. We report a small amount of results which are representative of the general "ndings regarding
R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354
349
Fig. 1. (A) Oscilloscope traces of typical extracellular potentials from one electrode. (B) Spike raster plot for simultaneously recorded neural ensemble during a normal recording session. A 20 s portion of a 15 min spontaneous recording is shown (g18:101298sp1).
long-term connectivity properties. The typical daily recordings provided from 30}70 single unit and multiunit clusters distributed across auditory cortex (Fig. 1) and were used to generate snapshots of daily connectivity. Snapshots spanning three consecutive recordings days were also obtained. 3.1. Snapshots demonstrate sparse functional connectivity Simultaneously recorded units in auditory cortex show sparse connectivity under both spontaneous conditions and under stimulation from simple auditory stimuli. We can estimate a connectivity &snapshot' in an individual animal by computing the "rst-order cross-correlations among the possible unit pairs. A sample of these crosscorrelograms, during a 10 min silent/non-stimulated period referenced to one unit
350
R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354
Fig. 2. Correlated activity distributed across the electrode array under spontaneous conditions in the cortex of a guinea pig. The same reference (unit 7a) is chosen for all of the correlation pairs. Crosscorrelograms are expressed in conditional spike probability. Bin width"10 ms; Recording duration of &15 min.
near the corner of the array illustrates the general "ndings (Fig. 2). Pairwise correlation, where present, consists of a positive peak near or surrounding the zero-latency point in the cross-correlogram (central mound). From this data set, we did not detect a distance e!ect on the correlation pattern across the neural ensemble. With auditory stimulation, correlations were found for units with both similar and dissimilar stimulus responses.
R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354
351
3.2. Snapshots compared from day-to-day show persistence of functional connectivity To investigate dynamic patterns of functional connectivity over successive recording days, we compare snapshots from each day for a single electrode. In Fig. 3c, we show that functional connectivity for two units on the same electrode persists over a three-day period. Note the near-coincident positive correlation (latency &1 ms) and unchanging correlation strength (peak conditional spike probability remains at &0.004) (Fig. 3c). Although these two units maintained consistent interval histograms (see 3a), the stimulus-driven PSTHs show variation (3b).
4. Discussion Chronic, multichannel recordings provide the only method for reliable detection and determination of long-term dynamic functional correlations within the auditory cortex. We have examined stimulus response properties and correlation strengths in best-neuron pairs in four animals. Our preliminary results from &snapshots' of functional connectivity in these subjects suggest a sparse connectivity between any given neuron pairs using inter-electrode spacing ranging from 250 to 2500 lm. However, despite this sparseness, we demonstrate that units displaying functional connectivity in a single snapshot can maintain that representation for up to three days. These results provide a framework upon which further investigations of functional dynamic connectivity can be developed. 4.1. Why is functional connectivity so sparse? Previous results from anaesthetized animals have suggested that signi"cant pairwise correlations between randomly chosen neurons in auditory cortex of cat occur with probability of &50% [3]. In our awake, but restrained guinea pigs, we found incidences of correlations in less than 10% of pairs. These discrepancies could suggest that animals in an awake state maintain more variable and dynamic connection patterns. Alternatively, our low correlation probabilities may relate to the use of less than optimal simple stimuli [2]. 4.2. What can dynamic functional correlation analysis reveal about cortical processing? In developing a chronic preparation capable of discriminating the same units over extended time periods [10], we have begun to investigate issues relating to dynamic functional correlation in simultaneously recorded neural ensembles. Such analysis meshes well with theories regarding neuronal population encoding such as the dynamic neuronal assemblies postulated by Vaadia et al. and Gerstein or the synchronized events suggested by Riehle et al. [5,8,9]. Our chronic approach allows for the determination of a hardwired framework upon which neuronal assemblies could be created or modi"ed. The sparseness of correlations however, suggests that alternative
352
R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354
Fig. 3. An example of correlation during noise bursts on an electrode that persists for three consecutive days. Row A contains the IHs for both units for each day. Row B shows the PSTHs (bin width"1 ms). Row C contains both the normalized cross-correlations (scaled in probability of a spike), and the averaged waveforms of the units for each day (bin width"0.25 ms).
R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354
353
methods may be required to fully investigate functional dynamic correlations among many neurons. Cortical mechanisms such as plasticity, learning or memory may best be described in terms of dynamic correlations.
References [1] E. Ahissar, E. Vaadia, M. Ahissar, H. Bergman, A. Arieli, M. Abeles, Dependence of cortical plasticity on correlated activity of single neurons and on behavioral context, Science 257 (1992) 1412}1415. [2] R.C. deCharms, D.T. Blake, M.M. Merzenich, Optimizing sound features for cortical neurons [see comments], Science 280 (1998) 1439}1443. [3] J.J. Eggermont, G.M. Smith, Neural connectivity only accounts for a small part of neural correlation in auditory cortex, Exp. Brain Res. 110 (1996) 379}391. [4] R.D. Frostig, Y. Gottlieb, E. Vaadia, M. Abeles, The e!ects of stimuli on the activity and functional connectivity of local neuronal groups in the cat auditory cortex, Brain Res. 272 (1983) 211}221. [5] G.L. Gerstein, P. Bedenbaugh, A.M.H.J. Aertsen, Neuronal assemblies, IEEE Trans. Biomed. Eng. 36 (1989) 4}14. [6] G. Palm, A.M.H.J. Aertsen, G.L. Gerstein, On the signi"cance of correlations among neuronal spike trains, Biol. Cybernet. 59 (1988) 1}11. [7] G.H. Recanzone, C.E. Schreiner, M.M. Merzenich, Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys, J. Neuroscience 13 (1) (1993) 87}103. [8] A. Riehle, S. Grun, M. Diesmann, A. Aertsen, Spike synchronization and rate modulation di!erentially involved in motor cortical function [see comments], Science 278 (1997) 1950}1953. [9] E. Vaadia, H. Bergman, M. Abeles, Neuronal activities related to higher brain functions } theoretical and experimental implications, IEEE Trans. Biomed. Eng. 36 (1989) 25}35. [10] J.C. Williams, R.L. Rennaker, D.R. Kipke, Stability of chronic multichannel neural recordings: implications for a long-term neural interface, Neurocomputing (1999), this volume.
R.S. Clement received a B.S. in Electrical Engineering from Ohio Northern University in May 1996. He is currently a graduate student at Arizona State University in the Bioengineering Program. His graduate work explores topics related to auditory processing in the cortex, and new studies in the cortical neural responses to cochlear implants. His interests include neuroprosthetics, hearing, and neuroscience.
R.S. Witte received a B.S. in Physics from the University of Arizona in August 1993. He is currently a graduate student at Arizona State University. His doctoral thesis explores the dynamics of cortical plasticity in auditory cortex of behaving animals. His major interests include "ber optics, neural prostheses, and evolving sensory processes of the brain.
354
R.S. Clement et al. /Neurocomputing 26}27 (1999) 347}354 P.J. Rousche received a Ph.D. degree in Bioengineering from the University of Utah in 1996. He is currently a faculty associate in Bioengineering at Arizona State University. His research interests include the collection and analysis of multiple channel electrode data in sensory cortex for the investigation of dynamic cortical processes such as plasticity, learning and memory, and the development of a neural prostheses.
D.R. Kipke received a Ph.D. degree in Bioengineering from the University of Michigan in 1991. He is currently an Associate Professor in Bioengineering at Arizona State University. His research involves auditory processing, neural prostheses, cortical plasticity, and neural implant technologies.