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Regional brain morphometry predicts memory rehabilitation outcome after traumatic brain injury.

Strangman GE, O'Neil-Pirozzi TM, Supelana C, Goldstein R, Katz DI, Glenn MB - Front Hum Neurosci (2010)

Bottom Line: Fifty individuals with TBI of all severities who reported having memory difficulties first underwent structural MRI scanning.We identified several brain regions that provided significant predictions of rehabilitation outcome, including the volume of the hippocampus, the lateral prefrontal cortex, the thalamus, and several subregions of the cingulate cortex.The prediction range of regional brain volumes were in some cases nearly equal in magnitude to prediction ranges provided by pretest scores on the outcome variable.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, Harvard Medical School Boston, MA, USA.

ABSTRACT
Cognitive deficits following traumatic brain injury (TBI) commonly include difficulties with memory, attention, and executive dysfunction. These deficits are amenable to cognitive rehabilitation, but optimally selecting rehabilitation programs for individual patients remains a challenge. Recent methods for quantifying regional brain morphometry allow for automated quantification of tissue volumes in numerous distinct brain structures. We hypothesized that such quantitative structural information could help identify individuals more or less likely to benefit from memory rehabilitation. Fifty individuals with TBI of all severities who reported having memory difficulties first underwent structural MRI scanning. They then participated in a 12 session memory rehabilitation program emphasizing internal memory strategies (I-MEMS). Primary outcome measures (HVLT, RBMT) were collected at the time of the MRI scan, immediately following therapy, and again at 1-month post-therapy. Regional brain volumes were used to predict outcome, adjusting for standard predictors (e.g., injury severity, age, education, pretest scores). We identified several brain regions that provided significant predictions of rehabilitation outcome, including the volume of the hippocampus, the lateral prefrontal cortex, the thalamus, and several subregions of the cingulate cortex. The prediction range of regional brain volumes were in some cases nearly equal in magnitude to prediction ranges provided by pretest scores on the outcome variable. We conclude that specific cerebral networks including these regions may contribute to learning during I-MEMS rehabilitation, and suggest that morphometric measures may provide substantial predictive value for rehabilitation outcome in other cognitive interventions as well.

No MeSH data available.


Related in: MedlinePlus

Brain morphometric parcellation results for an example subject, identifying key regions from this study. (A,B) Lateral and medial representations of the inflated cortical surface showing the 75 parcellated cortical regions per hemisphere. Only the left hemisphere is shown, laterally (A) and medially (B). (C) Coronal slice highlighting several subcortical structures automatically identified by the Freesurfer algorithms (thalamus, ventral diencephalon, hippocampus, basal ganglia, etc).
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Figure 1: Brain morphometric parcellation results for an example subject, identifying key regions from this study. (A,B) Lateral and medial representations of the inflated cortical surface showing the 75 parcellated cortical regions per hemisphere. Only the left hemisphere is shown, laterally (A) and medially (B). (C) Coronal slice highlighting several subcortical structures automatically identified by the Freesurfer algorithms (thalamus, ventral diencephalon, hippocampus, basal ganglia, etc).

Mentions: Structural MRI data was analyzed using Freesurfer v4.5 (Fischl et al., 2004), with the associated recon-all processing stream applied to each participant's pair of MPRAGE scans. In brief, the preprocessing stream consists of co-registering the two scans, non-uniform intensity normalization, Talairach transformation, skull stripping, volumetric labeling, tissue type segmentation, fitting of the two cortical surfaces (pial and white matter), and cortical parcellation. From this, data summaries for each cortical region are computed, including regional volume, surface area, curvature, and thickness; for details, including information on the 2009 version of the Destrieux atlas used for parcellation (see Fischl et al., 1999; Fischl et al., 2004; Destrieux et al., 2010; Schmansky, 2010). The Destrieux atlas was selected, as opposed to the Desikan atlas (Desikan et al., 2006), because it differentiates gyral and sulcal tissue. We deemed this difference potentially important in TBI where many gyri (as opposed to sulci) may contact the skull upon impact. In all, we collected – for each subject – gray matter volumes for 75 labeled cortical regions per hemisphere (see Figure 1) plus gray matter volume in 23 distinct subcortical regions. Of these latter 23 regions, we retained nine – specifically excluding total hemisphere measures, ventricles, vessels, image hypo-intensities, and the optic chiasm (Fischl et al., 2002). Upon completion of the automated processing, results were manually examined for clear failures to fit cortical surfaces (e.g., crossover of pial and white matter surfaces, errors related to inadequate skull stripping), major topological defects (holes or handles) in these surfaces, and failures in subcortical segmentation (e.g., distinct “islands” for contiguous regions). We sought to identify only gross errors for two reasons: (1) manual editing can cause bias, and (2) such editing is impractical in many clinical settings. Beyond the one failure to reach process completion (indicated earlier), no defects or failures were identified as requiring correction.


Regional brain morphometry predicts memory rehabilitation outcome after traumatic brain injury.

Strangman GE, O'Neil-Pirozzi TM, Supelana C, Goldstein R, Katz DI, Glenn MB - Front Hum Neurosci (2010)

Brain morphometric parcellation results for an example subject, identifying key regions from this study. (A,B) Lateral and medial representations of the inflated cortical surface showing the 75 parcellated cortical regions per hemisphere. Only the left hemisphere is shown, laterally (A) and medially (B). (C) Coronal slice highlighting several subcortical structures automatically identified by the Freesurfer algorithms (thalamus, ventral diencephalon, hippocampus, basal ganglia, etc).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2967347&req=5

Figure 1: Brain morphometric parcellation results for an example subject, identifying key regions from this study. (A,B) Lateral and medial representations of the inflated cortical surface showing the 75 parcellated cortical regions per hemisphere. Only the left hemisphere is shown, laterally (A) and medially (B). (C) Coronal slice highlighting several subcortical structures automatically identified by the Freesurfer algorithms (thalamus, ventral diencephalon, hippocampus, basal ganglia, etc).
Mentions: Structural MRI data was analyzed using Freesurfer v4.5 (Fischl et al., 2004), with the associated recon-all processing stream applied to each participant's pair of MPRAGE scans. In brief, the preprocessing stream consists of co-registering the two scans, non-uniform intensity normalization, Talairach transformation, skull stripping, volumetric labeling, tissue type segmentation, fitting of the two cortical surfaces (pial and white matter), and cortical parcellation. From this, data summaries for each cortical region are computed, including regional volume, surface area, curvature, and thickness; for details, including information on the 2009 version of the Destrieux atlas used for parcellation (see Fischl et al., 1999; Fischl et al., 2004; Destrieux et al., 2010; Schmansky, 2010). The Destrieux atlas was selected, as opposed to the Desikan atlas (Desikan et al., 2006), because it differentiates gyral and sulcal tissue. We deemed this difference potentially important in TBI where many gyri (as opposed to sulci) may contact the skull upon impact. In all, we collected – for each subject – gray matter volumes for 75 labeled cortical regions per hemisphere (see Figure 1) plus gray matter volume in 23 distinct subcortical regions. Of these latter 23 regions, we retained nine – specifically excluding total hemisphere measures, ventricles, vessels, image hypo-intensities, and the optic chiasm (Fischl et al., 2002). Upon completion of the automated processing, results were manually examined for clear failures to fit cortical surfaces (e.g., crossover of pial and white matter surfaces, errors related to inadequate skull stripping), major topological defects (holes or handles) in these surfaces, and failures in subcortical segmentation (e.g., distinct “islands” for contiguous regions). We sought to identify only gross errors for two reasons: (1) manual editing can cause bias, and (2) such editing is impractical in many clinical settings. Beyond the one failure to reach process completion (indicated earlier), no defects or failures were identified as requiring correction.

Bottom Line: Fifty individuals with TBI of all severities who reported having memory difficulties first underwent structural MRI scanning.We identified several brain regions that provided significant predictions of rehabilitation outcome, including the volume of the hippocampus, the lateral prefrontal cortex, the thalamus, and several subregions of the cingulate cortex.The prediction range of regional brain volumes were in some cases nearly equal in magnitude to prediction ranges provided by pretest scores on the outcome variable.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, Harvard Medical School Boston, MA, USA.

ABSTRACT
Cognitive deficits following traumatic brain injury (TBI) commonly include difficulties with memory, attention, and executive dysfunction. These deficits are amenable to cognitive rehabilitation, but optimally selecting rehabilitation programs for individual patients remains a challenge. Recent methods for quantifying regional brain morphometry allow for automated quantification of tissue volumes in numerous distinct brain structures. We hypothesized that such quantitative structural information could help identify individuals more or less likely to benefit from memory rehabilitation. Fifty individuals with TBI of all severities who reported having memory difficulties first underwent structural MRI scanning. They then participated in a 12 session memory rehabilitation program emphasizing internal memory strategies (I-MEMS). Primary outcome measures (HVLT, RBMT) were collected at the time of the MRI scan, immediately following therapy, and again at 1-month post-therapy. Regional brain volumes were used to predict outcome, adjusting for standard predictors (e.g., injury severity, age, education, pretest scores). We identified several brain regions that provided significant predictions of rehabilitation outcome, including the volume of the hippocampus, the lateral prefrontal cortex, the thalamus, and several subregions of the cingulate cortex. The prediction range of regional brain volumes were in some cases nearly equal in magnitude to prediction ranges provided by pretest scores on the outcome variable. We conclude that specific cerebral networks including these regions may contribute to learning during I-MEMS rehabilitation, and suggest that morphometric measures may provide substantial predictive value for rehabilitation outcome in other cognitive interventions as well.

No MeSH data available.


Related in: MedlinePlus