Morphology and spatial distribution of GABAergic neurons in cat primary auditory cortex (AI)

1994 ◽  
Vol 344 (3) ◽  
pp. 349-382 ◽  
Author(s):  
Jorge J. Prieto ◽  
Barbara A. Peterson ◽  
Jeffery A. Winer
Keyword(s):  
Spatial Distribution ◽  
Auditory Cortex ◽  
Primary Auditory Cortex ◽  
Gabaergic Neurons

Azimuthal sensitivity of neurons in primary auditory cortex of cats. II. Organization along frequency-band strips

1990 ◽  
Vol 64 (3) ◽  
pp. 888-902 ◽  
Author(s):  
R. Rajan ◽  
L. M. Aitkin ◽  
D. R. Irvine
Keyword(s):  
Spatial Distribution ◽  
Auditory Cortex ◽  
Frequency Band ◽  
Primary Auditory Cortex ◽  
Companion Paper ◽  
Significant Feature ◽  
Central Field ◽  
Cortical Surface ◽  
Peak Response ◽  
The Individual

1. The organization of azimuthal sensitivity of units across the dorsoventral extent of primary auditory cortex (AI) was studied in electrode penetrations made along frequency-band strips of AI. Azimuthal sensitivity for each unit was represented by a mean azimuth function (MF) calculated from all azimuth functions obtained to characteristic frequency (CF) stimuli at intensities 20 dB or more greater than threshold. MFs were classified as contrafield, ipsi-field, central-field, omnidirectional, or multipeaked, according to the criteria established in the companion paper (Rajan et al. 1990). 2. The spatial distribution of three types of MFs was not random across frequency-band strips: for contra-field, ipsi-field, and central-field MFs there was a significant tendency for clustering of functions of the same type in sequentially encountered units. Occasionally, repeated clusters of a particular MF type could be found along a frequency-band strip. In contrast, the spatial distribution of omnidirectional MFs along frequency-band strips appeared to be random. 3. Apart from the clustering of MF types, there were also regions along a frequency-band strip in which there were rapid changes in the type of MF encountered in units isolated over short distances. Most often such changes took the form of irregular, rapid juxtapositions of MF types. Less frequently such changes appeared to show more systematic changes from one type of MF to another type. In contrast to these changes in azimuthal sensitivity seen in electrode penetrations oblique to the cortical surface, much less change in azimuthal sensitivity was seen in the form of azimuthal sensitivity displayed by successively isolated units in penetrations made normal to the cortical surface. 4. To determine whether some significant feature or features of azimuthal sensitivity shifted in a more continuous and/or systematic manner along frequency-band strips, azimuthal sensitivity was quantified in terms of the peak-response azimuth (PRA) of the MFs of successive units and of the azimuthal range over which the peaks occurred in the individual azimuth functions contributing to each MF (the peak-response range). In different experiments shifts in these measures of the peaks in successively isolated units along a frequency-band strip were found generally to fall into one of four categories: 1) shifts across the entire frontal hemifield; 2) clustering in the contralateral quadrant; 3) clustering in the ipsilateral quadrant; and 4) clustering about the midline. In two cases more than one of these four patterns were found along a frequency-band strip.(ABSTRACT TRUNCATED AT 400 WORDS)

Functional topography of cat primary auditory cortex: distribution of integrated excitation

1990 ◽  
Vol 64 (5) ◽  
pp. 1442-1459 ◽  
Author(s):  
C. E. Schreiner ◽  
J. R. Mendelson
Keyword(s):  
Spatial Distribution ◽  
Auditory Cortex ◽  
Functional Organization ◽  
Primary Auditory Cortex ◽  
Response Magnitude ◽  
Response Threshold ◽  
Step Response ◽  
Frequency Independent ◽  
Multiple Unit ◽  
Excitatory Responses

1. Neuronal responses to tones and transient stimuli were mapped with microelectrodes in the primary auditory cortex (AI) of barbiturate anesthetized cats. Most of the dorsoventral extent of AI was mapped with multiple-unit recordings in the high-frequency domain (between 5.8 and 26.3 kHz) of all six studied cases. The spatial distributions of 1) sharpness of tuning measured with pure tones and 2) response magnitudes to a broadband transient were determined in each of three intensively studied cases. 2. The sharpness of tuning of integrated cluster responses was defined 10 dB above threshold (Q10 dB, integrated excitatory bandwidth). The spatial reconstructions revealed a frequency-independent maximum located near the center of the dorsoventral extent of AI. The sharpness of tuning gradually decreased toward the dorsal and ventral border of AI in all three cases. 3. The sharpness of tuning 40 dB above response threshold was also analyzed (Q40 dB). The Q40 dB values were less than one-half of the corresponding Q10 dB value. The spatial distribution showed a maximum in the center of AI, similar to the Q10 dB distribution. In two out of three cases, restricted additional maxima were recorded dorsal to the main maximum. Overall, Q10 dB and Q40 dB were only moderately correlated, indicating that the integrated excitatory bandwidth at higher stimulus levels can be influenced by additional mechanisms that are not active at lower levels. 4. The magnitude of excitatory responses to a broadband transient (frequency-step response) was determined. The normalized response magnitude varied between less than 1% and up to 100% relative to a characteristic frequency (CF) tone response. The step-response magnitude showed a systematic spatial distribution. An area dorsal to the Q10 dB maximum consistently showed the largest response magnitude surrounded by areas of lower responsivity. A second spatially more restricted maximum was recorded in the ventral-third of each map. Areas with high-transient responsiveness coincided with areas of broad integrated excitatory bandwidth at comparable stimulus levels. 5. The distribution of excitation produced by narrowband and broadband signals suggest that there exists a clear functional organization in the isofrequency domain of AI that is orthogonal to the main cochleotopic organization of the AI. Systematic spatial variations of the integrated excitatory bandwidth reflect underlying cortical processing capacities that may contribute to a parallel analysis of spectral complexity, e.g., spectral shape and contrast, at any given frequency.(ABSTRACT TRUNCATED AT 400 WORDS)

Topography of excitatory bandwidth in cat primary auditory cortex: single-neuron versus multiple-neuron recordings

1992 ◽  
Vol 68 (5) ◽  
pp. 1487-1502 ◽  
Author(s):  
C. E. Schreiner ◽  
M. L. Sutter
Keyword(s):  
Spatial Distribution ◽  
Auditory Cortex ◽  
Single Neuron ◽  
Primary Auditory Cortex ◽  
Single Neurons ◽  
Minimum Threshold ◽  
Multiple Unit ◽  
The Individual ◽  
Multiple Units ◽  
Ventral Border

1. The spatial distribution of the sharpness of tuning of single neurons along the dorsoventral extent of primary auditory cortex (AI) was studied. A sharpness of tuning gradient was initially obtained with multiple-unit recordings, and in combination with the cochleotopic organization, served as a frame of reference for the locations of single neurons. The frequency selectivity or "integrated excitatory bandwidth" of multiple units varied systematically along the dorsoventral extent of AI. The most sharply tuned unit clusters were found at the approximate center of the dorsoventral extent. A gradual broadening of the integrated excitatory bandwidth in both dorsal and ventral directions was consistently seen. 2. The multiple-unit measures of the bandwidth 10 (BW10) and 40 dB (BW40) above minimum threshold, pooled across several animals and expressed in octaves, were similar to those described within individual cases in cats. As in the individual animals, the bandwidth maps were V shaped with minima located at the approximate center of the dorsal-ventral extent of AI. The location of the minimum in the multiple-unit bandwidth map (i.e., the most sharply tuned area) was used as a reference point to pool single-neuron data across animals. 3. For single neurons, the dorsal half of the BW40 distribution showed a gradient paralleling that found for multiple units. For both single and multiple units, the average excitatory bandwidth increased at a rate of approximately 0.27 octaves/mm from the center of AI toward the dorsal fringe. Differing from the dorsal half of AI, the ventral half of AI showed no clear BW40 gradient for single units along its dorsoventral extent. At 40 dB above minimum threshold, most ventral neurons encountered were sharply tuned. By contrast, the multiple-unit BW40 showed a gradient similar to the dorsal half with 0.23 octaves/mm increasing from the center toward the ventral border of AI. 4. For single neurons, BW10 showed no clear systematic spatial distribution in AI. Neither the dorsal nor the ventral gradient was significantly different from zero slope, although the dorsal half showed a trend toward increasing BW10s. Contrasting single neurons, both dorsal and ventral halves of AI showed BW10 slopes for multiple units confirming a V-shaped map of the integrated excitatory bandwidth within the dorsoventral extent of AI. 5. On the basis of the distribution of the integrated (multiple-unit) excitatory bandwidth, AI was parceled into three regions: the dorsal gradient, the ventral gradient, and the central, narrowly tuned area.(ABSTRACT TRUNCATED AT 400 WORDS)

Intensity Tuning of Neurons in The Primary Auditory Cortex of Albino Mouse

2013 ◽  
Vol 40 (4) ◽  
pp. 365
Author(s):  
Qiao-Zhen QI ◽  
Wen-Juan SI ◽  
Feng LUO ◽  
Xin WANG
Keyword(s):  
Auditory Cortex ◽  
Primary Auditory Cortex

Influence of Primary Auditory Cortex in the Characterization of Autism Spectrum in Young Adults using Brain Connectivity Parameters and Deep Belief Networks: An fMRI Study

2020 ◽  
Vol 16 (9) ◽  
pp. 1059-1073
Author(s):  
Vidhusha Srinivasan ◽  
N. Udayakumar ◽  
Kavitha Anandan
Keyword(s):  
Functional Connectivity ◽  
Auditory Cortex ◽  
Language Processing ◽  
Brain Connectivity ◽  
Primary Auditory Cortex ◽  
Autism Spectrum ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
High Functioning ◽  
Low Functioning

Background: The spectrum of autism encompasses High Functioning Autism (HFA) and Low Functioning Autism (LFA). Brain mapping studies have revealed that autism individuals have overlaps in brain behavioural characteristics. Generally, high functioning individuals are known to exhibit higher intelligence and better language processing abilities. However, specific mechanisms associated with their functional capabilities are still under research. Objective: This work addresses the overlapping phenomenon present in autism spectrum through functional connectivity patterns along with brain connectivity parameters and distinguishes the classes using deep belief networks. Methods: The task-based functional Magnetic Resonance Images (fMRI) of both high and low functioning autistic groups were acquired from ABIDE database, for 58 low functioning against 43 high functioning individuals while they were involved in a defined language processing task. The language processing regions of the brain, along with Default Mode Network (DMN) have been considered for the analysis. The functional connectivity maps have been plotted through graph theory procedures. Brain connectivity parameters such as Granger Causality (GC) and Phase Slope Index (PSI) have been calculated for the individual groups. These parameters have been fed to Deep Belief Networks (DBN) to classify the subjects under consideration as either LFA or HFA. Results: Results showed increased functional connectivity in high functioning subjects. It was found that the additional interaction of the Primary Auditory Cortex lying in the temporal lobe, with other regions of interest complimented their enhanced connectivity. Results were validated using DBN measuring the classification accuracy of 85.85% for high functioning and 81.71% for the low functioning group. Conclusion: Since it is known that autism involves enhanced, but imbalanced components of intelligence, the reason behind the supremacy of high functioning group in language processing and region responsible for enhanced connectivity has been recognized. Therefore, this work that suggests the effect of Primary Auditory Cortex in characterizing the dominance of language processing in high functioning young adults seems to be highly significant in discriminating different groups in autism spectrum.

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