0
Original Contribution |

Increased Cerebral Metabolism After 1 Year of Deep Brain Stimulation in Alzheimer Disease FREE

Gwenn S. Smith, PhD; Adrian W. Laxton, MD; David F. Tang-Wai, MDCM, FRCPC; Mary Pat McAndrews, PhD; Andreea Oliviana Diaconescu, PhD; Clifford I. Workman, BS; Andres M. Lozano, MD, PhD, FRCSC
[+] Author Affiliations

Author Affiliations: Department of Psychiatry and Behavioral Sciences, Division of Geriatric Psychiatry and Neuropsychiatry, The Johns Hopkins University School of Medicine, The Johns Hopkins Bayview Medical Center (Dr Smith and Mr Workman), Baltimore, Maryland; Division of Neurosurgery (Drs Laxton and Lozano), Division of Neurology (Dr Tang-Wai), University Health Network Memory Clinic (Dr Tang-Wai), and Department of Psychology (Dr McAndrews), Toronto Western Hospital and Research Institute, University of Toronto, Ontario, Canada; and Laboratory for Social and Neural Systems Research, Institute for Empirical Research in Economics, University of Zürich, Switzerland (Dr Diaconescu).


Arch Neurol. 2012;69(9):1141-1148. doi:10.1001/archneurol.2012.590.
Text Size: A A A
Published online

Background The importance of developing unique, neural circuitry–based treatments for the cognitive and neuropsychiatric symptoms of Alzheimer disease (AD) was the impetus for a phase I study of deep brain stimulation (DBS) in patients with AD that targeted the fornix.

Objective To test the hypotheses that DBS would increase cerebral glucose metabolism in cortical and hippocampal circuits and that increased metabolism would be correlated with better clinical outcomes.

Design Open-label trial.

Setting Academic medical center.

Patients A total of 5 patients with mild, probable AD (1 woman and 4 men, with a mean [SD] age of 62.6 [4.2] years).

Intervention Deep brain stimulation of the fornix.

Main Outcome Measures All patients underwent clinical follow-up and high-resolution positron emission tomography studies of cerebral glucose metabolism after 1 year of DBS.

Results Functional connectivity analyses revealed that 1 year of DBS increased cerebral glucose metabolism in 2 orthogonal networks: a frontal-temporal-parietal-striatal-thalamic network and a frontal-temporal-parietal-occipital-hippocampal network. In similar cortical regions, higher baseline metabolism prior to DBS and increased metabolism after 1 year of DBS were correlated with better outcomes in global cognition, memory, and quality of life.

Conclusions Increased connectivity after 1 year of DBS is observed, which is in contrast to the decreased connectivity observed over the course of AD. The persistent cortical metabolic increases after 1 year of DBS were associated with better clinical outcomes in this patient sample and are greater in magnitude and more extensive in the effects on cortical circuitry compared with the effects reported for pharmacotherapy over 1 year in AD.

Figures in this Article

Alzheimer disease (AD) is one of the most common degenerative dementias, and more than 115 million new cases are projected worldwide in the next 40 years.1 Impairments in multiple domains of cognition (including episodic memory, executive dysfunction, and visuospatial processing) are observed.2 Neuropsychiatric symptoms (eg, depression, apathy, and agitation) occur in more than 98% of patients with AD.3 Treatment development has targeted specific neurotransmitters that are affected in AD (eg, acetylcholine and glutamate) and has targeted components of the “amyloid cascade” hypothesis.4,5 Medications developed to treat psychiatric illnesses in younger patients are used to treat neuropsychiatric symptoms in AD. The lack of significant efficacy of these interventions for cognitive and neuropsychiatric symptoms and, in some cases, the serious adverse effects associated with them underscore the urgent need for the development of safe and effective treatments, as well as the development of interventions to slow disease progression.

The cognitive deficits and neuropsychiatric symptoms in AD that involve degeneration of neural circuits, and the recent evidence from therapeutic and imaging studies regarding the role of β-amyloid (Aβ),6,7 highlight the importance of understanding changes in neural circuitry and developing treatments to modulate neuronal function in the affected neural circuits. Converging lines of evidence from the initial antiamyloid trials and Aβ imaging studies have shown that Aβ may be necessary but not sufficient to account for cognitive impairment and dementia progression.6,7 Dementia progression, despite brain clearance of Aβ by immunization, further underscores the importance of understanding and targeting treatments for the secondary consequences of Aβ deposition based on improving the function of specific neural circuits.6 Aβ deposition is not strongly correlated with cognitive deficits, and, despite clinical disease progression, only modest increases in Aβ deposition are observed in AD.7 In contrast to the Aβ imaging data, the functional activity of neural circuits, as measured by positron emission tomography (PET) studies of cerebral glucose metabolism, correlates with impairment in global cognition and specific cognitive domains, as well as neuropsychiatric symptoms, and shows progressive decreases over the course of AD.79

The importance of developing circuitry-based therapeutic approaches to modulate cortical and hippocampal networks affected in AD was the impetus for a recent phase I study10 of deep brain stimulation (DBS) of the fornix in patients with AD. This study10 showed that continuous DBS of the fornix produced sustained increases in cortical glucose metabolism after 1 month and after 1 year. The increases in metabolism are in contrast to the metabolic decreases observed over the course of AD and are more extensive and persistent over time relative to the effects of pharmacotherapy.8,9,11The present report focuses on the significance of cerebral metabolic increases observed in this unique data set. Specifically, the following questions were addressed: Do functional connectivity analyses reveal specific cortical and hippocampal networks increased by DBS that are affected in AD? Are baseline metabolism and the increase in metabolism with treatment correlated with better clinical outcomes? The hypotheses were tested that (1) cerebral metabolism would be increased in parietofrontal and hippocampal/parahippocampal-parietal networks after 1 year of DBS and (2) lesser metabolic deficits preoperatively and greater increases in metabolism in temporal and parietal regions would be correlated with better clinical outcomes.

The methods for patient recruitment, inclusion and exclusion criteria, clinical and neuropsychological assessments, and neurosurgical and neuroimaging procedures have been described previously in Laxton et al.10

SAMPLE

Six patients were recruited through the University Health Network Memory Clinic at the Toronto Western Hospital in Ontario, Canada. All of the patients underwent serial PET scans. Five of the 6 patients were studied using the same PET scanner. This report will focus on these 5 patients. The patients (1 woman and 4 men, with a mean [SD] age of 62.6 [4.2] years) met diagnostic criteria for probable AD,12 had a mean (SD) Mini-Mental State Examination13 score of 22.2 (5.1), and were on a stable dose of cholinesterase inhibitors for a minimum of 6 months. Exclusion criteria were having another neurologic or psychiatric diagnosis and having significant medical comorbidity or a structural brain abnormality detected on a magnetic resonance imaging scan. This study was approved by the research ethics boards of the University Health Network and the Centre for Addiction and Mental Health, both in Toronto, Ontario, Canada. Written informed consent was obtained from the patient and a surrogate who was either a spouse or a child. The trial was registered with the US National Institutes of Health ClinicalTrials.gov (NCT00658125).

SURGICAL AND CLINICAL METHODS

A complete description of the surgical procedures has been given in Laxton et al.10 The electrode target was chosen to lie 2 mm anterior and parallel to the vertical portion of the fornix within the hypothalamus, bilaterally. The ventral-most contact was 2 mm above the dorsal surface of the optic tract, approximately 5 mm from the midline. Two weeks after discharge from the hospital, the patients' stimulators were turned on. Stimulator settings and medications were kept constant for 12 months. Patients had neurological, neurosurgical, and neuropsychological assessments at baseline and at 1, 6, and 12 months after surgery. The outcome measures used in the present analysis are the Alzheimer's Disease Assessment Scale–cognitive subscale (ADAS-cog) score,14 a global measure of cognitive function that includes tests of declarative memory, orientation, praxis, and receptive and expressive language, and the Quality of Life–Alzheimer's Disease scale (QOL-AD) score.15

PET IMAGE ACQUISITION AND ANALYSIS

Positron emission tomography scans with the radiotracer [18F]-2-deoxy-2-fluoro-D-glucose to measure regional cerebral glucose metabolism were acquired preoperatively and after 1 month and 1 year of continuous DBS. The PET scans were performed on the CPS/Siemens high resolution research tomograph scanner. The glucose metabolism acquisition, quantification, and analysis methods have been described elsewhere.10,16 During the radiotracer uptake period, subjects were in a quiet, dimly lit room, with their eyes open and ears unoccluded. The preprocessing of the quantitative PET images, including PET-to-PET registration, normalization, and smoothing, was performed with statistical parametric mapping software version 5 (SPM5; Institute of Neurology, London, England).17

PET FUNCTIONAL CONNECTIVITY ANALYSIS

The partial least squares18 method was used to examine DBS modulation of functional connectivity within cortical and hippocampal networks. The term partial least squares refers to the computation of an optimal squares fit to part of a covariance structure that is attributable to the experimental manipulations or that relates to a given outcome measure or to a given seed/brain region of interest. The partial least squares method extracts condition-relevant spatial patterns that most optimally, as in the least square sense, represent the relationship between 2 blocks of data; in this case, the 2 seed regions and whole-brain glucose metabolism before and after DBS.

The seed regions were chosen to represent a limbic region more proximal to the DBS target and a cortical region more distal. Regions that are affected early in the course of AD and also demonstrate a significant DBS effect were chosen.10 Of the cortical regions, the precuneus was chosen because it is the region that shows the greatest metabolic deficits and Aβ deposition in the early course of AD. The parahippocampal gyrus is also affected in early AD and, in contrast, demonstrates relatively greater tau deposition than Aβ deposition.19 The specific seed regions are the left precuneus (coordinates x, y, z, respectively, in millimeters in Montreal Neurological Institute standard brain: −10, −45, 51) and the left hippocampal/parahippocampal gyrus (Montreal Neurological Institute coordinates: −26, −20, −21).

Functional connectivity analysis was performed across subjects and within each condition, before DBS, after 1 month of DBS, and after 1 year of DBS. The resulting matrix represented a within-condition brain-seed correlation matrix. Singular value decomposition was then applied to this brain-seed correlation matrix (Figure 1). Mathematically, singular value decomposition reexpresses a data matrix as a set of orthogonal singular vectors or latent variables (LVs), the number of which is equivalent to the total number of conditions multiplied by the total number of seeds. Each LV contains a pair of vectors relating metabolism to the 2 seeds before DBS, 1 month after DBS, and 1 year after DBS. For each LV, the 2 vectors are linked by a singular value, which represents the covariance between the 2 blocks of data and indicates the proportion of cross-block covariance that is accounted for by each LV. Thus, singular value decomposition produced 3 output matrices: the voxel saliences, the singular values, and the task saliences. In this case, the variations across task saliences indicate whether a given LV represents a similarity or difference in brain-seed correlations across the 3 conditions: before DBS, after 1 month of DBS, and after 1 year of DBS. The voxel saliences reflect the corresponding brain-seed correlation pattern across space (expressed across a collection of voxels).

Place holder to copy figure label and caption
Graphic Jump Location

Figure 1. Singular value decomposition used in the partial least squares functional connectivity analysis. Singular value decomposition is applied to the brain-seed correlation matrix. Mathematically, singular value decomposition reexpresses the brain-seed correlation matrix as a set of orthogonal singular vectors or latent variables. Each latent variable contains a pair of vectors connected by a singular value, which indicates the proportion of the cross-block covariance (ie, covariance between 2 blocks of data: the seed region and the rest of the brain). The pair of vectors (the voxel and task saliences) reflects a symmetrical relationship between the components of the experimental design related to the differing seed-voxel correlations expressed across all voxels in the brain on the one hand and the optimal (in the least squares sense) spatial pattern of voxels related to the identified experimental design components on the other.

Two complementary resampling techniques were used for statistical assessment of the changes in functional connectivity following DBS. First, permutation tests assessed whether the task saliences represented by the given LV were significantly different from random noise. This was accomplished by using sampling without replacement and by reassigning the order of the conditions to each subject. Second, the reliability of each voxel contribution to the LV was assessed using a bootstrap estimation of standard errors for the voxel saliences. The use of bootstrap estimation of standard errors eliminates the need to correct for multiple comparisons because the voxel saliences were calculated in a single mathematical step, on the whole brain at once. Statistical evaluation of brain-seed correlations was performed using an optimal number of 500 permutations20 and 80 bootstrap iterations.21

PET CORRELATION ANALYSES

The flexible factorial option in SPM5 was used to correlate baseline as well as change in cerebral metabolism from baseline, prior to DBS, to 1 month after DBS and 1 year after DBS. The glucose metabolic rates were normalized by scaling to a common mean value in all scans, after establishing that the global means did not differ significantly between conditions (P > .05). The baseline and change in metabolism after 1 month and 1 year of DBS were correlated with change in the following measures: ADAS-cog total and memory score and the QOL-AD score. The correlations were considered significant at a t value threshold greater than 3.51 (z > 2.98; P < .003, uncorrected for multiple independent comparisons; cluster size greater than 50 voxels). Brain locations are reported as x, y, z coordinates in millimeters in Montreal Neurological Institute space with approximate Brodmann areas identified by mathematical transformation of SPM5 coordinates into Talairach space.

The clinical outcomes were as follows: the mean (SD) ADAS-cog total scores were 19.2 (7.2) at baseline, 21.6 (9.2) after 1 month of DBS, and 23.9 (13.7) after 1 year of DBS (higher scores indicate worse performance). Although the group of 5 patients showed a mean worsening in ADAS-cog score of approximately 2 points every 6 months, 1 patient, the least affected at baseline, showed a 4-point lower score (improvement) after 1 year of DBS. The mean (SD) QOL-AD scores were 36.2 (2.3) at baseline, 34.6 (4.7) after 1 month of DBS, and 35.4 (6.4) after 1 year of DBS.

The functional connectivity analysis revealed a highly significant and reliable functional network after 1 year of DBS (LV = 42.89% cross-block covariance, P < .004; Figure 2A). Two distinct functional networks correlated with the left precuneus and the left hippocampal/parahippocampal seeds. For the 1-year DBS condition, compared with baseline, the brain regions that exhibited stable correlations with the left precuneus are listed in Table 1, and those that correlated with the left hippocampal/parahippocampal gyrus are listed in Table 2. For the baseline, prior to DBS, and 1-month DBS conditions, the correlation patterns were highly variable between subjects. The large confidence intervals around the mean correlation values were not statistically different from 0 in the baseline condition (compared with 1 in the 1-month DBS condition; data not shown).

Place holder to copy figure label and caption
Graphic Jump Location

Figure 2. Functional connectivity analyses of the cerebral metabolic changes after 1 year of deep brain stimulation (DBS). A, The results of using the partial least squares method on different seed regions (latent variable = 42.89% cross-block covariance; P < .004) are shown. The seed latent variable reflects the brain-seed covariance pattern at baseline and after 1 year of DBS. Task salience reflects differences in brain-seed functional connectivity patterns of the 2 seed regions, the left precuneus (coordinates x, y, z, respectively, in millimeters in Montreal Neurological Institute [MNI] standard brain: −10, −45, 51) and the left hippocampal/parahippocampal gyrus (MNI coordinates: −26, −20, −21), after 1 year of DBS. B, The network of brain regions that significantly correlated with the left precuneus seed (red) and the left hippocampal/parahippocampal seed (blue) are shown. Stable bootstrap ratios (BSRs) of 3 or greater that pertain to the brain-behavior covariance pattern are superimposed over a standard magnetic resonance imaging template. A frontal-temporal-parietal-striatal-thalamic network with the left precuneus as seed was associated with increased cerebral metabolism after 1 year of DBS. After 1 year of DBS, cerebral metabolism also increased in a frontal-temporal-parietal-occipital-hippocampal network with the hippocampal/parahippocampal gyrus as a seed. Error bars indicate 95% CIs.

Table Graphic Jump LocationTable 1. Local Maxima Extracted From Spatiotemporal Seed Partial Least Squares Method With the Left Precuneus as the Seed Regiona
Table Graphic Jump LocationTable 2. Local Maxima Extracted From Spatiotemporal Seed Partial Least Squares Method With the Left Hippocampal/Parahippocampal Gyrus as the Seed Regiona

In the functional connectivity analysis using the left precuneus as a seed, a frontal-temporal-parietal-striatal-thalamic network was identified. Significant positive correlations were observed with the anterior cingulate gyrus (Brodmann areas 24 and 32), superior, left medial, and inferior frontal gyri, gyrus rectus, bilateral insula, right superior temporal gyrus (Brodmann area 22), right fusiform gyrus, left putamen, thalamus, and cerebellum (bilateral except where indicated). The network of brain regions that significantly correlated with the left precuneus seed is indicated in red in Figure 2B and exhibits positive bootstrap ratios, which indicate a positive expression of the given task contrast (ie, positive correlations after DBS; Figure 2A). Note that the negative bootstrap ratios in Table 2 indicate positive correlations with the hippocampal/parahippocampal seed. This is because the sign of the bootstrap ratios must be interpreted along with the task contrast in Figure 2A (negative bootstrap ratio × negative task contrast = positive).

In the left hippocampal/parahippocampal gyrus seed analysis, a frontal-temporal-parietal-occipital-hippocampal network was identified. Significant positive correlations were observed with the superior frontal gyrus, paracentral lobule, right superior temporal gyrus, supramarginal gyrus, superior and inferior parietal lobules, precuneus, left superior and middle occipital gyri, and the right cuneus (all bilateral except where indicated). The network of brain regions that significantly correlated with the left hippocampal/parahippocampal seed is indicated in blue in Figure 2B and exhibits negative bootstrap ratios, which indicate a negative expression of the given task contrast (ie, positive correlations after DBS; Figure 2A).

The regional pattern of correlations between baseline and change in metabolism after 1 year of DBS with the ADAS-cog total score, the ADAS-cog memory score, and the QOL-AD score were similar. The results for the ADAS-cog total score will be presented. The correlations with the change in metabolism after 1 month of DBS were not significant.

A higher baseline (prior to DBS) metabolism in brain regions typically affected in AD was associated with improvement/less decline in global cognitive function (ADAS-cog total score; Table 3). The regions include anterior cortical regions (left anterior cingulate, bilateral superior and medial frontal gyri, and left insula), temporal cortical regions (bilateral superior and middle temporal gyri and left fusiform gyrus), parietal cortex (left precuneus, bilateral posterior cingulate, right supramarginal gyrus, and right inferior parietal lobule), and the bilateral cerebellum. Lower metabolism in regions typically spared in AD was associated with improvement/less decline in global cognitive function (ADAS-cog total score; Table 3). The regions include the bilateral precentral gyrus, the right postcentral gyrus, the bilateral cuneus, the bilateral putamen, and the bilateral thalamus (ventral-lateral nucleus).

Table Graphic Jump LocationTable 3. Correlations Between Baseline Cerebral Glucose Metabolism and 1-Year Change in ADAS-cog Total Score

Similar regions that showed baseline correlations also showed correlations between the increases in metabolism after 1 year of DBS and the improvement/less decline in ADAS-cog total score (Table 4). A similar pattern of correlations was obtained with the ADAS-cog memory score and the QOL-AD score (data not shown). The regions of significant correlations include anterior cortical regions (bilateral anterior cingulate, bilateral superior and medial frontal gyri, right inferior frontal gyrus, and right insula), temporal cortical regions (bilateral superior and left middle temporal cortices), parietal cortex (bilateral precuneus, bilateral posterior cingulate, and right inferior parietal lobule), and bilateral cerebellum.

Table Graphic Jump LocationTable 4. Correlations Between Change in Cerebral Glucose Metabolism and 1-Year Change in ADAS-cog Total Score

Similar regions that showed baseline correlations also showed correlations between decrease in metabolism 1 year after DBS and improvement/less decline in ADAS-cog score (Table 4). The regions include precentral gyrus, middle occipital gyrus, putamen, and thalamus (pulvinar: all bilateral).

After 1 year of continuous DBS, the functional connectivity analysis demonstrated increased cerebral metabolism in cortical-subcortical and cortical-hippocampal networks. In similar cortical regions, both a higher baseline metabolism and an increase after 1 year of DBS were correlated with less decline or improvement in global cognition, memory, and quality of life after 1 year of DBS. In contrast, after 1 month of DBS, both the functional connectivity and correlation analyses did not detect significant networks or correlations with clinical outcomes. There is a remarkable degree of overlap between the regions implicated in the functional connectivity analyses and the regions that showed significant increases and were correlated with better clinical outcomes, using independent statistical image analysis methods.

The functional connectivity analysis revealed a left precuneus network that included extensive connections with the anterior cingulate and frontal cortical regions and limited correlations with temporal regions (left superior temporal gyrus and fusiform gyrus), consistent with the demonstrated neuroanatomic connections between these regions.2225 These extensive cortical and subcortical connections suggest that the effects of DBS of the fornix may involve networks associated with memory and other aspects of cognition, such as executive function, affective processing, and motor programming.

In contrast, the functional connectivity analysis for the left hippocampal/parahippocampal gyrus network showed limited connections to the frontal cortex (superior frontal gyrus) and extensive connections to the parietal cortex and visual association cortex, also consistent with neuroanatomic connections.26,27 Reduced functional connectivity of the hippocampus with both anterior and posterior cortical regions, as well as longitudinal decreases in default mode subnetworks, have been reported in AD.28,29 In contrast, the present results show that 1 year of DBS is associated with improved functional connectivity, including posterior and ventral subdivisions of the default mode network.30 The networks identified, as well as the cortical regions that correlated with better clinical outcomes, include regions of significant AD pathology, the heteromodal association cortices.19

The observation that a higher preoperative regional glucose metabolism (including temporal and parietal areas affected in AD) is associated with better clinical outcomes (both cognitive and quality of life measures) supports the clinical observation that patients who are less cognitively impaired may benefit more from DBS.10 The areas “relatively spared” in AD, including precentral and postcentral gyri, cuneus, putamen, and thalamus, showed the opposite association, in that a lower metabolism was associated with better outcomes. Thus, cerebral glucose metabolism measures could be extremely useful in predicting DBS response or as a criterion for patient selection. Furthermore, such measures can be used as a guide to “tune the stimulation” to optimize connectivity and drive metabolism in specific areas.

Although cholinesterase inhibitors have also been shown to increase metabolism over several months, the effects do not tend to persist or to be as robust as the effects of DBS of the fornix.10,11,16 Furthermore, similar correlations with clinical outcomes have not been reported with cholinesterase inhibitors in AD. The study sample included relatively young, mildly affected patients with AD. The clinical and neurobiological effects and safety of DBS in older, more cognitively impaired patients cannot be inferred from the present study. Future studies in a larger sample size will permit the evaluation of the effects of DBS on multiple domains of cognitive and neuropsychiatric symptoms in AD relative to changes in underlying neural circuitry.

Based on preclinical studies of DBS of the Papez circuit, the pathophysiology of AD, and the factors that modulate cerebral glucose metabolism, the neurobiological mechanisms underlying the observed glucose metabolic effects may include effects on neurotransmission (eg, an increase in the level of acetylcholine or serotonin or the stabilization of glutamate), neurogenesis, or the release of neurotrophic factors (eg, brain-derived neurotrophic factor).3133 Aβ release has been linked to increased synaptic activity34 based on in vitro data. However, the effect of DBS on Aβ metabolism in patients with AD is not known and is a topic of current investigation in animal models. Future studies could use molecular imaging methods to test these underlying neurochemical or neuropathological mechanisms (eg, inflammation and Aβ). Neuroimaging studies performed during the course of DBS provide a unique opportunity to understand the consequences of fornix stimulation in AD on neural circuitry, neurochemical and pathological mechanisms, and behavior.

Correspondence: Gwenn S. Smith, PhD, Division of Geriatric Psychiatry and Neuropsychiatry, The Johns Hopkins University School of Medicine, The Johns Hopkins Bayview Medical Center, 5300 Alpha Commons Dr, 4th Floor, Baltimore, MD 21224 (gsmith95@jhmi.edu).

Accepted for Publication: March 6, 2012.

Published Online: May 7, 2012. doi:10.1001/archneurol.2012.590

Author Contributions:Study concept and design: Smith, Laxton, McAndrews, and Lozano. Acquisition of data: Smith, Laxton, and Tang-Wai. Analysis and interpretation of data: Smith, Laxton, Diaconescu, and Workman. Drafting of the manuscript: Smith and Workman. Critical revision of the manuscript for important intellectual content: Laxton, Tang-Wai, McAndrews, Diaconescu, and Lozano. Statistical analysis: Smith, Diaconescu, and Workman. Obtained funding: Laxton and Lozano. Administrative, technical, and material support: Laxton and Lozano.

Financial Disclosure: None reported.

Funding/Support: This work was supported by the Neurosurgical Research and Education Foundation (Dr Laxton), the Dana Foundation (Dr Lozano), the Krembil Neuroscience Discovery Fund (Dr Lozano), and grant 1 K02 MH01621 (Dr Smith).

Additional Information: Dr Lozano is a Tier 1 Canada Research Chair in Neuroscience and holds the R. R. Tasker Chair in Functional Neurosurgery at the University of Toronto.

Wimo A, Prince M. World Alzheimer Report 2010: The Global Economic Impact of Dementia. London, England: Alzheimer's Disease International; 2010
Albert M. Neuropsychology of Alzheimer's disease.  Handb Clin Neurol. 2008;88:511-525
PubMed
Steinberg M, Shao H, Zandi P,  et al; Cache County Investigators.  Point and 5-year period prevalence of neuropsychiatric symptoms in dementia: the Cache County Study.  Int J Geriatr Psychiatry. 2008;23(2):170-177
PubMed   |  Link to Article
Neugroschl J, Sano M. An update on treatment and prevention strategies for Alzheimer's disease.  Curr Neurol Neurosci Rep. 2009;9(5):368-376
PubMed
Golde TE, Schneider LS, Koo EH. Anti-aβ therapeutics in Alzheimer's disease: the need for a paradigm shift.  Neuron. 2011;69(2):203-213
PubMed
Holmes C, Boche D, Wilkinson D,  et al.  Long-term effects of Abeta42 immunisation in Alzheimer's disease: follow-up of a randomised, placebo-controlled phase I trial.  Lancet. 2008;372(9634):216-223
PubMed
Rabinovici GD, Jagust WJ. Amyloid imaging in aging and dementia: testing the amyloid hypothesis in vivo.  Behav Neurol. 2009;21(1):117-128
PubMed
Smith GS, de Leon MJ, George AE,  et al.  Topography of cross-sectional and longitudinal glucose metabolic deficits in Alzheimer's disease. Pathophysiologic implications.  Arch Neurol. 1992;49(11):1142-1150
PubMed
Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM. Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer's disease treatment studies.  Am J Psychiatry. 2002;159(5):738-745
PubMed
Laxton AW, Tang-Wai DF, McAndrews MP,  et al.  A phase I trial of deep brain stimulation of memory circuits in Alzheimer's disease.  Ann Neurol. 2010;68(4):521-534
PubMed
Stefanova E, Wall A, Almkvist O,  et al.  Longitudinal PET evaluation of cerebral glucose metabolism in rivastigmine treated patients with mild Alzheimer's disease.  J Neural Transm. 2006;113(2):205-218
PubMed
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease.  Neurology. 1984;34(7):939-944
PubMed
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician.  J Psychiatr Res. 1975;12(3):189-198
PubMed
Rosen WG, Mohs RC, Davis KL. A new rating scale for Alzheimer's disease.  Am J Psychiatry. 1984;141(11):1356-1364
PubMed
Logsdon RG, Gibbons LE, McCurry SM, Teri L. Assessing quality of life in older adults with cognitive impairment.  Psychosom Med. 2002;64(3):510-519
PubMed
Smith GS, Kramer E, Ma Y,  et al.  Cholinergic modulation of the cerebral metabolic response to citalopram in Alzheimer's disease.  Brain. 2009;132(pt 2):392-401
PubMed
Penny WD, ed, Friston KJ, ed, Ashburner JT, ed, Kiebel SJ, ed, Nichols TE, edStatistical Parametric Mapping: The Analysis of Functional Brain Images. London, England: Academic Press; 2007
McIntosh AR, Lobaugh NJ. Partial least squares analysis of neuroimaging data: applications and advances.  Neuroimage. 2004;23:(suppl 1)  S250-S263
PubMed
Arnold SE, Hyman BT, Flory J, Damasio AR, Van Hoesen GW. The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer's disease.  Cereb Cortex. 1991;1(1):103-116
PubMed
Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples.  Hum Brain Mapp. 2002;15(1):1-25
PubMed
Efron B, Tibshirani R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy.  Stat Sci. 1986;1:54-77
PubMed  |  Link to Article
Cavada C, Goldman-Rakic PS. Posterior parietal cortex in rhesus monkey: II, evidence for segregated corticocortical networks linking sensory and limbic areas with the frontal lobe.  J Comp Neurol. 1989;287(4):422-445
PubMed
Parvizi J, Van Hoesen GW, Buckwalter J, Damasio A. Neural connections of the posteromedial cortex in the macaque.  Proc Natl Acad Sci U S A. 2006;103(5):1563-1568
PubMed
Yeterian EH, Pandya DN. Striatal connections of the parietal association cortices in rhesus monkeys.  J Comp Neurol. 1993;332(2):175-197
PubMed
Schmahmann JD, Pandya DN. Anatomical investigation of projections from thalamus to posterior parietal cortex in the rhesus monkey: a WGA-HRP and fluorescent tracer study.  J Comp Neurol. 1990;295(2):299-326
PubMed
Vincent JL, Kahn I, Van Essen DC, Buckner RL. Functional connectivity of the macaque posterior parahippocampal cortex.  J Neurophysiol. 2010;103(2):793-800
PubMed
Lavenex P, Suzuki WA, Amaral DG. Perirhinal and parahippocampal cortices of the macaque monkey: projections to the neocortex.  J Comp Neurol. 2002;447(4):394-420
PubMed
Allen G, Barnard H, McColl R,  et al.  Reduced hippocampal functional connectivity in Alzheimer disease.  Arch Neurol. 2007;64(10):1482-1487
PubMed
Damoiseaux JS, Prater KE, Miller BL, Greicius MD. Functional connectivity tracks clinical deterioration in Alzheimer's disease.  Neurobiol Aging. 2012;33(4):828.e19-828.e30
PubMed
Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. Functional-anatomic fractionation of the brain's default network.  Neuron. 2010;65(4):550-562
PubMed
Laxton AW, Dostrovsky JO, Lozano AM. Stimulation physiology in functional neurosurgery. In: Lozano AM, Gildenberg PL, Tasker RR, eds. Textbook of Stereotactic and Functional Neurosurgery. 2nd ed. New York, NY: Springer Verlag; 2009:1383-1399
Stone SS, Teixeira CM, Devito LM,  et al.  Stimulation of entorhinal cortex promotes adult neurogenesis and facilitates spatial memory.  J Neurosci. 2011;31(38):13469-13484
PubMed
Encinas JM, Hamani C, Lozano AM, Enikolopov G. Neurogenic hippocampal targets of deep brain stimulation.  J Comp Neurol. 2011;519(1):6-20
PubMed
Kamenetz F, Tomita T, Hsieh H,  et al.  APP processing and synaptic function.  Neuron. 2003;37(6):925-937
PubMed

Figures

Place holder to copy figure label and caption
Graphic Jump Location

Figure 1. Singular value decomposition used in the partial least squares functional connectivity analysis. Singular value decomposition is applied to the brain-seed correlation matrix. Mathematically, singular value decomposition reexpresses the brain-seed correlation matrix as a set of orthogonal singular vectors or latent variables. Each latent variable contains a pair of vectors connected by a singular value, which indicates the proportion of the cross-block covariance (ie, covariance between 2 blocks of data: the seed region and the rest of the brain). The pair of vectors (the voxel and task saliences) reflects a symmetrical relationship between the components of the experimental design related to the differing seed-voxel correlations expressed across all voxels in the brain on the one hand and the optimal (in the least squares sense) spatial pattern of voxels related to the identified experimental design components on the other.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 2. Functional connectivity analyses of the cerebral metabolic changes after 1 year of deep brain stimulation (DBS). A, The results of using the partial least squares method on different seed regions (latent variable = 42.89% cross-block covariance; P < .004) are shown. The seed latent variable reflects the brain-seed covariance pattern at baseline and after 1 year of DBS. Task salience reflects differences in brain-seed functional connectivity patterns of the 2 seed regions, the left precuneus (coordinates x, y, z, respectively, in millimeters in Montreal Neurological Institute [MNI] standard brain: −10, −45, 51) and the left hippocampal/parahippocampal gyrus (MNI coordinates: −26, −20, −21), after 1 year of DBS. B, The network of brain regions that significantly correlated with the left precuneus seed (red) and the left hippocampal/parahippocampal seed (blue) are shown. Stable bootstrap ratios (BSRs) of 3 or greater that pertain to the brain-behavior covariance pattern are superimposed over a standard magnetic resonance imaging template. A frontal-temporal-parietal-striatal-thalamic network with the left precuneus as seed was associated with increased cerebral metabolism after 1 year of DBS. After 1 year of DBS, cerebral metabolism also increased in a frontal-temporal-parietal-occipital-hippocampal network with the hippocampal/parahippocampal gyrus as a seed. Error bars indicate 95% CIs.

Tables

Table Graphic Jump LocationTable 1. Local Maxima Extracted From Spatiotemporal Seed Partial Least Squares Method With the Left Precuneus as the Seed Regiona
Table Graphic Jump LocationTable 2. Local Maxima Extracted From Spatiotemporal Seed Partial Least Squares Method With the Left Hippocampal/Parahippocampal Gyrus as the Seed Regiona
Table Graphic Jump LocationTable 3. Correlations Between Baseline Cerebral Glucose Metabolism and 1-Year Change in ADAS-cog Total Score
Table Graphic Jump LocationTable 4. Correlations Between Change in Cerebral Glucose Metabolism and 1-Year Change in ADAS-cog Total Score

References

Wimo A, Prince M. World Alzheimer Report 2010: The Global Economic Impact of Dementia. London, England: Alzheimer's Disease International; 2010
Albert M. Neuropsychology of Alzheimer's disease.  Handb Clin Neurol. 2008;88:511-525
PubMed
Steinberg M, Shao H, Zandi P,  et al; Cache County Investigators.  Point and 5-year period prevalence of neuropsychiatric symptoms in dementia: the Cache County Study.  Int J Geriatr Psychiatry. 2008;23(2):170-177
PubMed   |  Link to Article
Neugroschl J, Sano M. An update on treatment and prevention strategies for Alzheimer's disease.  Curr Neurol Neurosci Rep. 2009;9(5):368-376
PubMed
Golde TE, Schneider LS, Koo EH. Anti-aβ therapeutics in Alzheimer's disease: the need for a paradigm shift.  Neuron. 2011;69(2):203-213
PubMed
Holmes C, Boche D, Wilkinson D,  et al.  Long-term effects of Abeta42 immunisation in Alzheimer's disease: follow-up of a randomised, placebo-controlled phase I trial.  Lancet. 2008;372(9634):216-223
PubMed
Rabinovici GD, Jagust WJ. Amyloid imaging in aging and dementia: testing the amyloid hypothesis in vivo.  Behav Neurol. 2009;21(1):117-128
PubMed
Smith GS, de Leon MJ, George AE,  et al.  Topography of cross-sectional and longitudinal glucose metabolic deficits in Alzheimer's disease. Pathophysiologic implications.  Arch Neurol. 1992;49(11):1142-1150
PubMed
Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM. Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer's disease treatment studies.  Am J Psychiatry. 2002;159(5):738-745
PubMed
Laxton AW, Tang-Wai DF, McAndrews MP,  et al.  A phase I trial of deep brain stimulation of memory circuits in Alzheimer's disease.  Ann Neurol. 2010;68(4):521-534
PubMed
Stefanova E, Wall A, Almkvist O,  et al.  Longitudinal PET evaluation of cerebral glucose metabolism in rivastigmine treated patients with mild Alzheimer's disease.  J Neural Transm. 2006;113(2):205-218
PubMed
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease.  Neurology. 1984;34(7):939-944
PubMed
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician.  J Psychiatr Res. 1975;12(3):189-198
PubMed
Rosen WG, Mohs RC, Davis KL. A new rating scale for Alzheimer's disease.  Am J Psychiatry. 1984;141(11):1356-1364
PubMed
Logsdon RG, Gibbons LE, McCurry SM, Teri L. Assessing quality of life in older adults with cognitive impairment.  Psychosom Med. 2002;64(3):510-519
PubMed
Smith GS, Kramer E, Ma Y,  et al.  Cholinergic modulation of the cerebral metabolic response to citalopram in Alzheimer's disease.  Brain. 2009;132(pt 2):392-401
PubMed
Penny WD, ed, Friston KJ, ed, Ashburner JT, ed, Kiebel SJ, ed, Nichols TE, edStatistical Parametric Mapping: The Analysis of Functional Brain Images. London, England: Academic Press; 2007
McIntosh AR, Lobaugh NJ. Partial least squares analysis of neuroimaging data: applications and advances.  Neuroimage. 2004;23:(suppl 1)  S250-S263
PubMed
Arnold SE, Hyman BT, Flory J, Damasio AR, Van Hoesen GW. The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer's disease.  Cereb Cortex. 1991;1(1):103-116
PubMed
Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples.  Hum Brain Mapp. 2002;15(1):1-25
PubMed
Efron B, Tibshirani R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy.  Stat Sci. 1986;1:54-77
PubMed  |  Link to Article
Cavada C, Goldman-Rakic PS. Posterior parietal cortex in rhesus monkey: II, evidence for segregated corticocortical networks linking sensory and limbic areas with the frontal lobe.  J Comp Neurol. 1989;287(4):422-445
PubMed
Parvizi J, Van Hoesen GW, Buckwalter J, Damasio A. Neural connections of the posteromedial cortex in the macaque.  Proc Natl Acad Sci U S A. 2006;103(5):1563-1568
PubMed
Yeterian EH, Pandya DN. Striatal connections of the parietal association cortices in rhesus monkeys.  J Comp Neurol. 1993;332(2):175-197
PubMed
Schmahmann JD, Pandya DN. Anatomical investigation of projections from thalamus to posterior parietal cortex in the rhesus monkey: a WGA-HRP and fluorescent tracer study.  J Comp Neurol. 1990;295(2):299-326
PubMed
Vincent JL, Kahn I, Van Essen DC, Buckner RL. Functional connectivity of the macaque posterior parahippocampal cortex.  J Neurophysiol. 2010;103(2):793-800
PubMed
Lavenex P, Suzuki WA, Amaral DG. Perirhinal and parahippocampal cortices of the macaque monkey: projections to the neocortex.  J Comp Neurol. 2002;447(4):394-420
PubMed
Allen G, Barnard H, McColl R,  et al.  Reduced hippocampal functional connectivity in Alzheimer disease.  Arch Neurol. 2007;64(10):1482-1487
PubMed
Damoiseaux JS, Prater KE, Miller BL, Greicius MD. Functional connectivity tracks clinical deterioration in Alzheimer's disease.  Neurobiol Aging. 2012;33(4):828.e19-828.e30
PubMed
Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. Functional-anatomic fractionation of the brain's default network.  Neuron. 2010;65(4):550-562
PubMed
Laxton AW, Dostrovsky JO, Lozano AM. Stimulation physiology in functional neurosurgery. In: Lozano AM, Gildenberg PL, Tasker RR, eds. Textbook of Stereotactic and Functional Neurosurgery. 2nd ed. New York, NY: Springer Verlag; 2009:1383-1399
Stone SS, Teixeira CM, Devito LM,  et al.  Stimulation of entorhinal cortex promotes adult neurogenesis and facilitates spatial memory.  J Neurosci. 2011;31(38):13469-13484
PubMed
Encinas JM, Hamani C, Lozano AM, Enikolopov G. Neurogenic hippocampal targets of deep brain stimulation.  J Comp Neurol. 2011;519(1):6-20
PubMed
Kamenetz F, Tomita T, Hsieh H,  et al.  APP processing and synaptic function.  Neuron. 2003;37(6):925-937
PubMed

Correspondence

CME
Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.
NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).
Submit a Comment

Multimedia

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Articles Related By Topic
Related Topics
PubMed Articles
JAMAevidence.com

Users' Guides to the Medical Literature
Clinical Resolution

Users' Guides to the Medical Literature
Clinical Scenario