Content-Specific Codes of Parametric Vibrotactile Working Memory in Humans

To understand how the brain handles mentally represented information flexibly in the absence of sensory stimulation, working memory (WM) studies have been essential. A seminal finding in monkey research is that neurons in the prefrontal cortex (PFC) retain stimulus-specific information when vibrotactile frequencies were memorized. A direct mapping between monkey studies and human research is still controversial. Although oscillatory signatures, in terms of frequency-dependent parametric beta-band modulation, have been observed recently in human EEG studies, the content specificity of these representations in terms of multivariate pattern analysis has not yet been shown. Here, we used fMRI in combination with multivariate classification techniques to determine which brain regions retain information during WM. In a retro-cue delayed-match-to-sample task, human subjects memorized the frequency of vibrotactile stimulation over a 12 s delay phase. Using an assumption-free whole-brain searchlight approach, we tested with support vector regression which brain regions exhibited multivariate parametric WM codes of the maintained frequencies during the WM delay. Interestingly, our analysis revealed an overlap with regions previously identified in monkeys composed of bilateral premotor cortices, supplementary motor area, and the right inferior frontal gyrus as part of the PFC. Therefore, our results establish a link between the WM codes found in monkeys and those in humans and emphasize the importance of the PFC for information maintenance during WM also in humans. SIGNIFICANCE STATEMENT Working memory (WM) research in monkeys has identified a network of regions, including prefrontal regions, to code stimulus-specific information when vibrotactile frequencies are memorized. Here, we performed an fMRI study during which human subjects had to memorize vibratory frequencies in parallel to previous monkey research. Using an assumption-free, whole-brain searchlight decoding approach, we identified for the first time regions in the human brain that exhibit multivariate patterns of activity to code the vibratory frequency parametrically during WM. Our results parallel previous monkey findings and show that the supplementary motor area, premotor, and the right prefrontal cortex are involved in vibrotactile WM coding in humans.

[1]  Felix Blankenburg,et al.  Supramodal Parametric Working Memory Processing in Humans , 2012, The Journal of Neuroscience.

[2]  Mark D'Esposito,et al.  The effect of rehearsal rate and memory load on verbal working memory , 2015, NeuroImage.

[3]  Felix Blankenburg,et al.  Parametric Alpha- and Beta-Band Signatures of Supramodal Numerosity Information in Human Working Memory , 2014, The Journal of Neuroscience.

[4]  Clayton E Curtis,et al.  Prioritized Maps of Space in Human Frontoparietal Cortex , 2012, The Journal of Neuroscience.

[5]  P. Roelfsema,et al.  The Distributed Nature of Working Memory , 2017, Trends in Cognitive Sciences.

[6]  P. Goldman-Rakic Cellular basis of working memory , 1995, Neuron.

[7]  R. Romo,et al.  Decoding a Perceptual Decision Process across Cortex , 2010, Neuron.

[8]  Bradley R Postle,et al.  The cognitive neuroscience of visual short-term memory , 2015, Current Opinion in Behavioral Sciences.

[9]  Goldman-Rakic Cellular Basis of Working Memory Review , 2022 .

[10]  Simon B. Eickhoff,et al.  Assignment of functional activations to probabilistic cytoarchitectonic areas revisited , 2007, NeuroImage.

[11]  R. Romo,et al.  Conversion of sensory signals into perceptual decisions , 2013, Progress in Neurobiology.

[12]  Chris I. Baker,et al.  Multi-Voxel Decoding and the Topography of Maintained Information During Visual Working Memory , 2016, Front. Syst. Neurosci..

[13]  Dirk Ostwald,et al.  Imaging tactile imagery: Changes in brain connectivity support perceptual grounding of mental images in primary sensory cortices , 2014, NeuroImage.

[14]  Dwight J. Kravitz,et al.  Goal-dependent dissociation of visual and prefrontal cortices during working memory , 2013, Nature Neuroscience.

[15]  Frank Tong,et al.  Imagery and visual working memory: one and the same? , 2013, Trends in Cognitive Sciences.

[16]  Simon B. Eickhoff,et al.  A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data , 2005, NeuroImage.

[17]  M. R. Riley,et al.  Role of Prefrontal Persistent Activity in Working Memory , 2016, Front. Syst. Neurosci..

[18]  R. Romo,et al.  Neuronal Correlates of a Perceptual Decision in Ventral Premotor Cortex , 2004, Neuron.

[19]  Gustav Theodor Fechner,et al.  Elements of psychophysics , 1966 .

[20]  A. Nobre,et al.  Prioritizing Information during Working Memory: Beyond Sustained Internal Attention , 2017, Trends in Cognitive Sciences.

[21]  R. Romo,et al.  Neuronal correlates of parametric working memory in the prefrontal cortex , 1999, Nature.

[22]  Philippe Pinel,et al.  Distributed and Overlapping Cerebral Representations of Number, Size, and Luminance during Comparative Judgments , 2004, Neuron.

[23]  Jakob Heinzle,et al.  Decoding different roles for vmPFC and dlPFC in multi-attribute decision making , 2011, NeuroImage.

[24]  Arno Villringer,et al.  Neural Correlates of Vibrotactile Working Memory in the Human Brain , 2006, The Journal of Neuroscience.

[25]  R. Romo,et al.  Touch and go: decision-making mechanisms in somatosensation. , 2001, Annual review of neuroscience.

[26]  Felix Blankenburg,et al.  Working memory coding of analog stimulus properties in the human prefrontal cortex. , 2014, Cerebral cortex.

[27]  S. Dehaene,et al.  Representation of number in the brain. , 2009, Annual review of neuroscience.

[28]  J. Haynes A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives , 2015, Neuron.

[29]  Jarrod A. Lewis-Peacock,et al.  Neural Evidence for the Flexible Control of Mental Representations. , 2015, Cerebral cortex.

[30]  Rainer Goebel,et al.  Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[31]  A. Baddeley Working memory: theories, models, and controversies. , 2012, Annual review of psychology.

[32]  T. Pasternak,et al.  Working memory in primate sensory systems , 2005, Nature Reviews Neuroscience.

[33]  Felix Blankenburg,et al.  Maintenance and manipulation of somatosensory information in ventrolateral prefrontal cortex , 2014, Human brain mapping.

[34]  John-Dylan Haynes,et al.  Decoding the Contents of Visual Short-Term Memory from Human Visual and Parietal Cortex , 2012, The Journal of Neuroscience.

[35]  Sergey V. Fogelson,et al.  Network structure and dynamics of the mental workspace , 2013, Proceedings of the National Academy of Sciences.

[36]  Martin N. Hebart,et al.  The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data , 2015, Front. Neuroinform..

[37]  John-Dylan Haynes,et al.  Decoding complex flow-field patterns in visual working memory , 2014, NeuroImage.

[38]  Adam C. Riggall,et al.  The Relationship between Working Memory Storage and Elevated Activity as Measured with Functional Magnetic Resonance Imaging , 2012, The Journal of Neuroscience.

[39]  B. Postle,et al.  The cognitive neuroscience of working memory. , 2007, Annual review of psychology.

[40]  Adam C. Riggall,et al.  Distributed Patterns of Activity in Sensory Cortex Reflect the Precision of Multiple Items Maintained in Visual Short-Term Memory , 2013, The Journal of Neuroscience.

[41]  Marcia K. Johnson,et al.  Memory: Enduring Traces of Perceptual and Reflective Attention , 2011, Neuron.

[42]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[43]  Jarrod A. Lewis-Peacock,et al.  Multiple neural states of representation in short-term memory? It’s a matter of attention , 2014, Front. Hum. Neurosci..

[44]  R. C. Oldfield The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[45]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[46]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[47]  Edward F. Ester,et al.  Parietal and Frontal Cortex Encode Stimulus-Specific Mnemonic Representations during Visual Working Memory , 2015, Neuron.

[48]  R. Romo,et al.  Neuronal correlates of sensory discrimination in the somatosensory cortex. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[49]  J. Fuster,et al.  Mnemonic neuronal activity in somatosensory cortex. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[50]  Kartik K. Sreenivasan,et al.  Revisiting the role of persistent neural activity during working memory , 2014, Trends in Cognitive Sciences.

[51]  J. Fuster,et al.  Visuo-tactile cross-modal associations in cortical somatosensory cells. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[52]  Vincent Walsh A theory of magnitude: common cortical metrics of time, space and quantity , 2003, Trends in Cognitive Sciences.

[53]  S. Kosslyn Mental images and the Brain , 2005, Cognitive neuropsychology.

[54]  R. Romo,et al.  Neuronal correlates of decision-making in secondary somatosensory cortex , 2002, Nature Neuroscience.

[55]  I. Toni,et al.  Shared Representations for Working Memory and Mental Imagery in Early Visual Cortex , 2013, Current Biology.

[56]  Andreas Nieder,et al.  Coding of abstract quantity by ‘number neurons’ of the primate brain , 2012, Journal of Comparative Physiology A.

[57]  Ranulfo Romo,et al.  Sense, memory, and decision-making in the somatosensory cortical network , 2012, Current Opinion in Neurobiology.

[58]  S Goldman-RakicP.,et al.  Cellular Basis of Working Memory Review , 1995 .