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Neural correlates of working memory
- 1. Neural Correlates of Working Memory (Andrews, 2014, August 26) Working memory mentally holds and process incoming information from the sensory organs. Houde, et al., 2010 ozella.brundidge@gmail.com 8/20/2017 1
- 2. Memory is Directly Related to the Mental Development Index Recognition Recall Memory is vital for storage, consolidation, and retrieving representations of objects and events. Preterms’ are vulnerable to injury from ischemic hypoxia, characterized by a reduction of blood to the brain. They perform poorer than full-term controls on Pattern Recognition, and significantly poorer on the Delayed Matched-to-Sample and Spatial Recognition assessments. 2 Aspects of Memory that are Dependent on the Hippocampus and Associated Structures are two (Rose, Feldman, Jankowski, & Van Rossem, 2011; Volpe, 1995) & ozella.brundidge@gmail.com 8/20/2017
- 3. A previous study revealed a significant relationship between hippocampal volumes and Working Memory deficits in 2 year-old toddlers who were born very premature (30 weeks gestation or weighing <1250 grams). (Beauchamp, Thompson, Howard, Doyle, Egan, Inder, Anderson, 2008) Premature Birth Affect Hippocampus Volume and Working Memory Ability 3 ozella.brundidge@gmail.com 8/20/2017
- 4. Working Memory Impairment is Associated mostly with Diffused White Matter Damage Damage to Diffused White Matter • Is common in 2nd and 3rd trimester births • Affects the connectivity between distant cortical areas (Lubsen, Vohr, Myers, Hampson, Lacadie, Schneider, Katz, Constable, & Ment, 2011) 4 Dreamstime, 2015 ozella.brundidge@gmail.com 8/20/2017
- 5. Central Executive (CE) Master controller which focuses our attention Phonological Loop (PL) Temporary storage and rehearsal of verbal and auditory information Visuo-Spatial Sketchpad Temporary storage of visual and spatial information Episodic Buffer Allows the CE, PL, and VSS to interact with each other and information in long term memory to facilitate comprehension Components of Working Memory (Memorizingmusic.com, February 4, 2013; Baddeley, 2012 in 2013) (VSS) ozella.brundidge@gmail.com 8/20/2017 5
- 6. Working Memory Model 6 Sensory Memory Working Memory Attention Central Executive Phonological Loop Visuo-spatial Sketchpad Episodic Buffer Long Term Memory Encoding Retrieval (Memorizingmusic.com, Feburary 4, 2013; Baddeley, 2012 in 2013) ozella.brundidge@gmail.com 8/20/2017
- 7. (Sousa, 2005; Jensen, 2009; Jobard, et al., 2003; Barde, et al., 2012; Leff, Schofield,, Crinion et al. 2009; Wright, et al., 2012) 7 Working Memory and the Wernicke’s area also become functional around the age of 10 Months. They are able to attach meaning to toddlers’ newly acquired vocabulary. (Amy Lynn, n.d.) By 10 to 12 Months old the Toddler’s Brain is able to Distinguish and Remember Phonemes ozella.brundidge@gmail.com 8/20/2017
- 8. Left Wernicke’s Area (pSTG) is a Shared Substrate for Auditory Short-Term Memory and Speech Comprehension (Barde, Yeatman, Lee, Glover, & Feldman, 2012; Leff, Schofield, Crinion, Seghier, Grogan, Green, & Price, 2009; Wright, Stamatakis, & Tyler, 2012) Immediate Memory Working Memory 8 Navigate Your Life, n.d. pSTG – Posterior Superior Temporal Gyrus Two Types of Auditory Short-Term Memory ozella.brundidge@gmail.com 8/20/2017
- 9. (Sousa, 2005; Jensen, 2009; Jobard, et al., 2003; Hickock, Bushsbaum, & Humphries, 2003; Buschsaum & D’Eposito, 2008; Bavelier, Newport, & Supalla, 2003) Working Memory and Wernicke’s Area Assist in the Development of Vocabulary Skills 9 (Bavelier, 2003) ozella.brundidge@gmail.com 8/20/2017
- 10. Working Memory Tasks Activate the Supramarginal Gyrus (SMG) Bilaterally Phonemic Representations activates stores Sentences (Deschamps, Baum, & Gracco, 2013; Meyer, Obleser, Anwander, & Friederici, 2012; Paulesu, Frith, & Frackowiak, 1993 in Weems & Reggia, 2006; Stoeckel, Gough, Watkins, & Devlin, 2009) (MCAT Basics, 2015) SMGActivatedduring ozella.brundidge@gmail.com 8/20/2017 10
- 11. Auditory Verbal Short-Term Memory and Language Processing are Mediated by the same Areas on the Left Perisylvian Cortex (Koenigs, Acheson, Barbey, Solomon, Postle, Grafman, 2011; Bavelier, Newport, & Supalla, 2003; Vigneau, et al., 2006) 11 Posterior Superior Temple Region of the Perisylvian Cortex Inferior Frontal Region of the Left Perisylvian Cortex (2003) ozella.brundidge@gmail.com 8/20/2017
- 12. Areas of the Left Perisylvian Cortex Mediate Auditory-Verbal Short-Term Memory The yellow color indicates voxels where damage is associated with Significantly Lower Digit Span. Significant voxels were found only in left perisylvian cortex. 12 (Koenigs, Acheson, Barbey, Solomon, Postle, & Grafman, 2011, Figure 1) Voxel-based Lesion Symptom Mapping (VLSM) Analysis revealed Areas where Damage is Associated with Significantly Lower Digit Span Scores. ozella.brundidge@gmail.com 8/20/2017
- 13. Adolescents Exhibit Greater Activity than Young Adults in the Motor and Premotor Cortical Areas during Verbal Working Memory Tasks 13 Adolescents (11-15 yo) performed a parametric of Sternberg’s working memory task while undergoing a functional magnetic resonance imaging (MRI) procedure. Imaging data revealed activation in the frontal lobe’s Motor (BA4) and Premotor (BA 6) areas. (O’Hare, Lu, Houston, Bookheimer, & Sowell, 2008) Figure 3 panel C, p. 14 ozella.brundidge@gmail.com 8/20/2017
- 14. Adolescents Exhibit Greater Activity than Children in the Parietal Cortical Area and the Cerebellum during Verbal Working Memory Tasks 14 (O’Hare, Lu, Houston, Bookheimer, & Sowell, 2008) Figure 3 panel C, p. 14 Adolescents (11-15 yo) performed a parametric of Sternberg’s working memory task while undergoing a functional magnetic resonance imaging (MRI) procedure. Imaging data revealed activation in the parietal lobe and the cerebellum. ozella.brundidge@gmail.com 8/20/2017
- 15. Premotor Cortex is Brodmann Area (BA 6) The lower premotor area is activated during • Working memory • Silent rehearsal (Vigneau, Beaucousin, Herve, Duffau, Crivello, Houde, Mazoyer, & Tzourio-Mazoyer 2006) 15 Lower Premotor Area (www.imgarcade.com, 2017) Frontal Lobe ozella.brundidge@gmail.com 8/20/2017
- 16. Linking Word Ordering to the Broca's area (BA 44) and Temporary Storage to the Left Temporo-Parietal Region during Sentence Processing Tasks Left Temporo-Parietal activation correlates with test takers’ Digit Span outcome. The Broca’s area is not activated during verbal working memory tasks. (Meyers, Obleser, Anwander, & Friederici, 2012) 16ozella.brundidge@gmail.com 8/20/2017
- 17. Spatial Working Memory Tasks Activate the Right Dorsolateral and Medial Prefrontal Grey Matter • This region is also called the Guenon (BA 9). • It is part of the Superior Frontal-Intraparietal Network. • DTI revealed relational activities in this region while participants performed spatial working memory tasks. (Klingberg, 2006) 17 (2006) Wikipedia, November 22, 2014 Guenon (BA 9) ozella.brundidge@gmail.com 8/20/2017
- 18. Visuo-Spatial Working Memory Cytoarchitecture Visuo-Spatial Working Memory is dependent upon the integrity of the: • Superior frontal-intraparietal network • Primary motor cortex (PMC) • Somatosensory cortex (SSC) • Multiple grey and white matter regions in the frontal and parietal cortices. (Klingberg, 2006; Desikan, Segonne, Fischl, Quinn, Dickerson, Blacker, Buckner, et al., 2006) 18ozella.brundidge@gmail.com 8/20/2017
- 19. (Vigneau, Beaucousin, Herve, Duffau, Crivello, Houde, Mazoyer, & Tzourio-Mazoyer, 2006) Phonology Semantics Sentence Processing The Efficiency of the Working Memory Perception-Action Loops can be Measured using the WISC-IV and WAIS Tests 19 WISC-IV – Wechsler Intelligence Scale for Children; WAIS - Wechsler Adult Intelligence Scale There are Working Memory Perception-Action Loops Identifiable for each Language Component. ozella.brundidge@gmail.com 8/20/2017
- 20. Letter-Number Sequencing Subtest Assess Auditory Sequential Working Memory The tasks on this subtest involve Mental Manipulation, Visual-Spatial Imaging, and Processing Speed. The tester is read a sequence of numbers and letters, and then asked to recall the numbers in ascending order and the letters in alphabetical order. 20Case Study Student’s Grade 6 IEPozella.brundidge@gmail.com 8/20/2017
- 21. Digit Span Digit Span Subtest Measures Auditory-Verbal Short-Term Memory Storage Capacity Attention Sequencing Skills Concentration (Koenigs, Acheson, Barbey, Solomon, Postle, & Grafman, 2011; Kay, School Psychologist 10/2006 IEP) 21 Requires Hernandez, 5/26/2017 ozella.brundidge@gmail.com 8/20/2017
- 22. End of Neural Correlates of Working Memory (Andrews, 2014, August 26) Working memory mentally holds and process incoming information. Houde, et al., 2010 ozella.brundidge@gmail.com 8/20/2017 22
Editor's Notes
- Federal Officials Order Medicaid To Cover Autism Services. http://www.tilrc.org/assests/news/0814news/0814bene26.html
- Consolidation is a neurological process that involves gradually converting information from short-term memory into long-term memory. Remember, short-term memories are only stored for about 20 to 30 seconds. Cerebral hypoxia can also be classified by the cause of the reduced brain oxygen: ... Ischemic hypoxia ( or "stagnanthypoxia") – Reduced brain oxygen is caused by inadequate blood flow to the brain.
- VSS-e.g. objects and their locations
- Baddeley, A.D., Working memory: theories, models, and controversies, Annu. Rev. Psychol. 63 (2012):1-29 1. a central executive is the master controller which focuses our attention; 2. phonological loop for temporary storage and rehearsal of verbal and auditory information; 3. a visuo-spatial sketchpad for temporary storage of visual and spatial information, e.g. objects and their locations; and 4. An episodic buffer allows these different elements to interact with each other and information in long term memory to facilitate comprehension.
- Amy Lynn (). Vintage cute baby talking on phone puppy dog. https://www.pinterest.com/pin/481181541408683040/
- left superior temporal gyrus is a shared substrate for auditory short-term memory and speech comprehension Slide 20 Wernicke's area: Receptive language processing (Jackson, 1999; Weems & Reggia, 2006; Cerruti, 2010; Frey & Fischer, 2010; Robson, et al., 2010; Trollinger, et al., 2010; Bach, et al., 20131; Barde, et al., 2012; Syntactic comprehension –left posterior temporal lobe (Wright, Stamatakis, & Tyler et al., 2012) )
- Bavelier, D., Newport, E. L., & Supalla, T. (2003, January 1). Children Need Natural Languages, Signed or Spoken. The Dana Foundation. http://www.dana.org/Cerebrum/Default.aspx?id=39306 Buchsbaum, B. R. & D'Esposito (2008). Repetition suppression and reactivation in auditory-verbal short-term recognition memory. Cerebral Cortex, 19, 1474-1485. doi:10.1093/cercor/bhn186
- Slide 35 Supramarginal overview
- By: Daphne Bavelier, Ph.D., Elissa L. Newport, Ph.D., and Ted Supalla, Ph.D. Bavelier, Newport, & Supalla, 2003 Xiang, et al., (2010) wrote that “…the inferior parietal lobule is connected by large bundles of nerve fibers to both Broca's area and Wernicke's area (Catani et al. 2005; Parker et al. 2005; Powell et al. 2006). a functional connectivity topology can be observed in the perisylvian language networks connecting the 3 sub regions of Broca's complex (pars opercularis, pars triangularis, and pars orbitalis) to the the left middle frontal, parietal, and temporal areas. Xiang and colleagues’ (2010) results support the assumption of the functional division for phonology, syntax, and semantics of Broca's complex as proposed by the memory, unification, and control (MUC) model and indicated a topographical functional organization in the perisylvian language networks, which suggests a possible division of labor for phonological, syntactic, and semantic function in the left frontal, parietal, and temporal areas.
- Subjects performed a parametric verbal Sternberg working memory task to examine cerebro-cerebellar (Desmond et al., 2003, Chen and Desmond, 2005). Stimuli were generated using MacStim 3.0 psychological experimentation software (CogState, West Melbourne, Victoria, Australia) and were visually presented to subjects in the scanner using magnet-compatible 3-D goggles (Resonance Technology, Northridge, CA). Stimuli consisted of a horizontal array of uppercase consonant letters and pound (#) symbols. There were three types of arrays: high WM load (6 letters), medium WM load (3 letters and three “#” symbols), and low WM load (one letter and five “#” symbols). Stimuli and Task Parameters Subjects performed a parametric verbal Sternberg working memory task, similar to that used by Desmond and colleagues to examine cerebro-cerebellar networks in adults (Desmond et al., 2003, Chen and Desmond, 2005). Stimuli consisted of a horizontal array of uppercase consonant letters and pound (#) symbols. There were three types of arrays: high WM load (6 letters), medium WM load (3 letters and three “#” symbols), and low WM load (one letter and five “#” symbols). For the medium and low load stimuli, letter array position was counterbalanced across all possible array positions. Within the task, the overall appearance of a given letter was counterbalanced, as was the order of presentation of load conditions. Stimuli were generated using MacStim 3.0 psychological experimentation software (CogState, West Melbourne, Victoria, Australia) and were visually presented to subjects in the scanner using magnet-compatible 3-D goggles (Resonance Technology, Northridge, CA). Subjects were instructed to remember the letters in each array. Letter arrays were presented for 1.5 seconds, followed by a three second delay (see Figure 1). Following this delay, a lowercase letter probe stimulus was presented for 1.5s. For all load conditions, the probe stimulus matched the previously presented letter(s) on half of the trials. Position of the probe stimulus within the array was counterbalanced. Subjects were instructed to respond by pushing a button with their index finger if the probe stimulus matched an array letter and to push a button with their middle finger when the probe stimulus did not match an array letter. The task consisted of four blocks of each load condition (12 WM blocks total), with four trials in each 24-second block. Thirteen 12-second rest blocks, in which subjects fixated on a cross hair, alternated with the WM blocks, for a total task time of 7 minutes, 24 seconds. Subjects were trained on the task prior to the start of the scanning session. Training included a verbal description of the task and then two practice runs, each containing 5 trials. All subjects were able to perform the task prior to the start of the scanning session. Once the subject was in the scanner, task instructions were reviewed again before the start of the task. Data acquisition Functional imaging data were collected on the UCLA Division of Brain Mapping’s researchdedicated 3 Tesla Siemens Allegra head-only magnet. Multi-slice echo-planar imaging (EPI) was used with a gradient echo EPI sequence. We used a TR of 3 sec, with a TE of 25. Slice thickness was 3 mm with a 1 mm skip, 36 total slices, with 64x64 pixels yielding a resolution in-plane of 3mm with whole-brain acquisition. A high-resolution T2-weighted EPI volume was collected in the anterior commissure-posterior commissure plane (slice thickness=3mm, 36 total axial slices covering the entire brain with 1mm gaps between slices, TR=5000ms, TE=33ms, flip angle=90 degrees, matrix size 128 x 128 with 1.5 x 1.5 mm in-plane voxel dimensions), coplanar with the functional scan, to allow for spatial registration of each subject’s data into a standard coordinate space. Image and Statistical Analysis Functional imaging data were analyzed using FSL (FMRIB’s Software Library, http://www.fmrib.ox.ac.uk/fsl/index.html, (Smith et al., 2004). Data were corrected for possible motion artifacts by co-registering each BOLD image in the time series to the middle volume in the series using a 6-parameter rigid-body transformation. Volumes that had more than 2mm of head motion were excluded and the remaining time series data were analyzed. For a given subject, if excessive head motion occurred on more than 17 volumes within each task condition, that subject was excluded. Slice timing correction was applied to correct each voxel’s time series given that slices were acquired in an interleaved fashion. Data were spatially smoothed using a 6 mm (FWHM) Gaussian kernel and a high pass filter cutoff period of 50 seconds was imposed. Finally, all data were registered in a two-step process. First, each subject’s EPI data were registered to their own T2-weighted structural image with a 6- parameter transformation for within subject analyses, and then to the MNI-152 standard space template with a 12-parameter transformation for group averaging (Jenkinson and Smith, 2001, Jenkinson et al., 2002). Single subject analyses were carried out using FMRIB’s fMRI Expert Analysis Tool (FEAT, version 5.63). In these analyses, low, medium, and high WM loads were modeled separately. The hemodynamic response function (HRF) was specified as a gamma variate function (mean lag of 6 seconds) and convolved with the modeled components. Statistical analysis of the time series data was carried out using FMRIB’s Improved Linear Model (FILM). A voxel-wise general linear model was applied so that each voxel’s time course was individually fit to the model with local autocorrelation correction (Woolrich et al., 2001). Overall WM responses were determined by comparing WM activity (collapsed across loads) with rest. Orthogonal contrasts were then used to compute linear and nonlinear response functions across load conditions. Linear functions were modeled as [−1 0 1] and identified voxels that showed increasing activity with increasing WM load. Nonlinear functions were modeled as [−1 2 −1] to investigate the extent to which the change between easy and medium loads differed from the change between medium and hard loads. Z statistic images were thresholded using clusters determined by Z > 1.7 and a (corrected) cluster significance threshold of p = 0.05. Higher-level group analysis was carried out using FLAME (FMRIB’s Local Analysis of Mixed Effects). FLAME takes each subject’s time series data and models all subjects in each group (children, adolescents, young adults). Thus, group average responses for the linear and nonlinear contrasts were derived. Thus, a one-way analysis of variance (ANOVA) with the factor of age group was used to examine group differences in activation for these contrasts.
- Subjects performed a parametric verbal Sternberg working memory task to examine cerebro-cerebellar (Desmond et al., 2003, Chen and Desmond, 2005). Stimuli were generated using MacStim 3.0 psychological experimentation software (CogState, West Melbourne, Victoria, Australia) and were visually presented to subjects in the scanner using magnet-compatible 3-D goggles (Resonance Technology, Northridge, CA). Stimuli consisted of a horizontal array of uppercase consonant letters and pound (#) symbols. There were three types of arrays: high WM load (6 letters), medium WM load (3 letters and three “#” symbols), and low WM load (one letter and five “#” symbols). Stimuli and Task Parameters Subjects performed a parametric verbal Sternberg working memory task, similar to that used by Desmond and colleagues to examine cerebro-cerebellar networks in adults (Desmond et al., 2003, Chen and Desmond, 2005). Stimuli consisted of a horizontal array of uppercase consonant letters and pound (#) symbols. There were three types of arrays: high WM load (6 letters), medium WM load (3 letters and three “#” symbols), and low WM load (one letter and five “#” symbols). For the medium and low load stimuli, letter array position was counterbalanced across all possible array positions. Within the task, the overall appearance of a given letter was counterbalanced, as was the order of presentation of load conditions. Stimuli were generated using MacStim 3.0 psychological experimentation software (CogState, West Melbourne, Victoria, Australia) and were visually presented to subjects in the scanner using magnet-compatible 3-D goggles (Resonance Technology, Northridge, CA). Subjects were instructed to remember the letters in each array. Letter arrays were presented for 1.5 seconds, followed by a three second delay (see Figure 1). Following this delay, a lowercase letter probe stimulus was presented for 1.5s. For all load conditions, the probe stimulus matched the previously presented letter(s) on half of the trials. Position of the probe stimulus within the array was counterbalanced. Subjects were instructed to respond by pushing a button with their index finger if the probe stimulus matched an array letter and to push a button with their middle finger when the probe stimulus did not match an array letter. The task consisted of four blocks of each load condition (12 WM blocks total), with four trials in each 24-second block. Thirteen 12-second rest blocks, in which subjects fixated on a cross hair, alternated with the WM blocks, for a total task time of 7 minutes, 24 seconds. Subjects were trained on the task prior to the start of the scanning session. Training included a verbal description of the task and then two practice runs, each containing 5 trials. All subjects were able to perform the task prior to the start of the scanning session. Once the subject was in the scanner, task instructions were reviewed again before the start of the task. Data acquisition Functional imaging data were collected on the UCLA Division of Brain Mapping’s researchdedicated 3 Tesla Siemens Allegra head-only magnet. Multi-slice echo-planar imaging (EPI) was used with a gradient echo EPI sequence. We used a TR of 3 sec, with a TE of 25. Slice thickness was 3 mm with a 1 mm skip, 36 total slices, with 64x64 pixels yielding a resolution in-plane of 3mm with whole-brain acquisition. A high-resolution T2-weighted EPI volume was collected in the anterior commissure-posterior commissure plane (slice thickness=3mm, 36 total axial slices covering the entire brain with 1mm gaps between slices, TR=5000ms, TE=33ms, flip angle=90 degrees, matrix size 128 x 128 with 1.5 x 1.5 mm in-plane voxel dimensions), coplanar with the functional scan, to allow for spatial registration of each subject’s data into a standard coordinate space. Image and Statistical Analysis Functional imaging data were analyzed using FSL (FMRIB’s Software Library, http://www.fmrib.ox.ac.uk/fsl/index.html, (Smith et al., 2004). Data were corrected for possible motion artifacts by co-registering each BOLD image in the time series to the middle volume in the series using a 6-parameter rigid-body transformation. Volumes that had more than 2mm of head motion were excluded and the remaining time series data were analyzed. For a given subject, if excessive head motion occurred on more than 17 volumes within each task condition, that subject was excluded. Slice timing correction was applied to correct each voxel’s time series given that slices were acquired in an interleaved fashion. Data were spatially smoothed using a 6 mm (FWHM) Gaussian kernel and a high pass filter cutoff period of 50 seconds was imposed. Finally, all data were registered in a two-step process. First, each subject’s EPI data were registered to their own T2-weighted structural image with a 6- parameter transformation for within subject analyses, and then to the MNI-152 standard space template with a 12-parameter transformation for group averaging (Jenkinson and Smith, 2001, Jenkinson et al., 2002). Single subject analyses were carried out using FMRIB’s fMRI Expert Analysis Tool (FEAT, version 5.63). In these analyses, low, medium, and high WM loads were modeled separately. The hemodynamic response function (HRF) was specified as a gamma variate function (mean lag of 6 seconds) and convolved with the modeled components. Statistical analysis of the time series data was carried out using FMRIB’s Improved Linear Model (FILM). A voxel-wise general linear model was applied so that each voxel’s time course was individually fit to the model with local autocorrelation correction (Woolrich et al., 2001). Overall WM responses were determined by comparing WM activity (collapsed across loads) with rest. Orthogonal contrasts were then used to compute linear and nonlinear response functions across load conditions. Linear functions were modeled as [−1 0 1] and identified voxels that showed increasing activity with increasing WM load. Nonlinear functions were modeled as [−1 2 −1] to investigate the extent to which the change between easy and medium loads differed from the change between medium and hard loads. Z statistic images were thresholded using clusters determined by Z > 1.7 and a (corrected) cluster significance threshold of p = 0.05. Higher-level group analysis was carried out using FLAME (FMRIB’s Local Analysis of Mixed Effects). FLAME takes each subject’s time series data and models all subjects in each group (children, adolescents, young adults). Thus, group average responses for the linear and nonlinear contrasts were derived. Thus, a one-way analysis of variance (ANOVA) with the factor of age group was used to examine group differences in activation for these contrasts.
- http://www.bing.com/images/search?view=detailV2&ccid=YO337DV5&id=B1F5BC5BF48CF41CE1246F61D4DF3D3D01166428&thid=OIP.YO337DV56Z4LC37jIAk--AEEEi&q=Premotor+Cortex&simid=607996366680557122&selectedIndex=1&ajaxhist=0
- Neuropsychological Tests Short-Term Memory—Digit Span is the quintessential neuropsychological test of verbal STM. In Digit Span (Wechsler, 1997), the subject hears a sequence of digits and attempts to repeat the sequence in order. Sequence length is increased until the subject can no longer correctly repeat the sequence. The maximum sequence length for each subject is interpreted as the subject’s digit span (Koenigs, Acheson, Barbey, Solomon, Postle, Grafman, 2011).
- Diffusion tensor imaging (DTI) measures the characteristics of white matter.
- Lesions Associated with Digit span (scores of 4 or less) - the most severe impairments in auditory-verbal STM capacity were associated with relatively large lesions involving left perisylvian cortex. (Koenigs et al., 2011). employing voxel-based lesion symptom mapping (VLSM) (Bates et al., 2003). largest cluster of significant voxels (z score 3.5 significant) was located in superior and middle temporal gyri in the mid- to posterior region of left temporal lobe, while smaller clusters were located in left inferior frontal gyrus and left inferior parietal lobule.
- Hernandez, B. (2017, May 26). Tips for remembering how to spell Wednesday and other tricky words. Retrieved from https://www.thoughtco.com/spelling-tricky-words-wednesday-1833083 Neuropsychological Tests Short-Term Memory—Digit Span is the quintessential neuropsychological test of verbal STM. In Digit Span (Wechsler, 1997), the subject hears a sequence of digits and attempts to repeat the sequence in order. Sequence length is increased until the subject can no longer correctly repeat the sequence. The maximum sequence length for each subject is interpreted as the subject’s digit span (Koenigs, Acheson, Barbey, Solomon, Postle, Grafman, 2011).
- Federal Officials Order Medicaid To Cover Autism Services. http://www.tilrc.org/assests/news/0814news/0814bene26.html