Federated learning for medical image analysis: A survey

Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field.

Gene specific effects on brain volume and cognition of TMEM106B in frontotemporal lobar degeneration

Background and Objectives

TMEM106B has been proposed as a modifier of disease risk in FTLD-TDP, particularly in GRN mutation carriers. Furthermore, TMEM106B has been investigated as a disease modifier in the context of healthy aging and across multiple neurodegenerative diseases. The objective of this study is to evaluate and compare the effect of TMEM106B on gray matter volume and cognition in each of the common genetic FTD groups and in sporadic FTD patients.

Methods

Participants were enrolled through the ARTFL/LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) study, which includes symptomatic and presymptomatic individuals with a pathogenic mutation in C9orf72, GRN, MAPT, VCP, TBK1, TARDBP, symptomatic non-mutation carriers, and non-carrier family controls. All participants were genotyped for the TMEM106B rs1990622 SNP. Cross-sectionally, linear mixed-effects models were fitted to assess an association between TMEM106B and genetic group interaction with each outcome measure (gray matter volume and UDS3-EF for cognition), adjusting for education, age, sex and CDR®+NACC-FTLD sum of boxes. Subsequently, associations between TMEM106B and each outcome measure were investigated within the genetic group. For longitudinal modeling, linear mixed-effects models with time by TMEM106B predictor interactions were fitted.

Results

The minor allele of TMEM106B rs1990622, linked to a decreased risk of FTD, associated with greater gray matter volume in GRN mutation carriers under the recessive dosage model. This was most pronounced in the thalamus in the left hemisphere, with a retained association when considering presymptomatic GRN mutation carriers only. The minor allele of TMEM106B rs1990622 also associated with greater cognitive scores among all C9orf72 mutation carriers and in presymptomatic C9orf72 mutation carriers, under the recessive dosage model.

Discussion

We identified associations of TMEM106B with gray matter volume and cognition in the presence of GRN and C9orf72 mutations. This further supports TMEM106B as modifier of TDP-43 pathology. The association of TMEM106B with outcomes of interest in presymptomatic GRN and C9orf72 mutation carriers could additionally reflect TMEM106B's impact on divergent pathophysiological changes before the appearance of clinical symptoms.

Reliability and Validity of Smartphone Cognitive Testing for Frontotemporal Lobar Degeneration

Importance

Frontotemporal lobar degeneration (FTLD) is relatively rare, behavioral and motor symptoms increase travel burden, and standard neuropsychological tests are not sensitive to early-stage disease. Remote smartphone-based cognitive assessments could mitigate these barriers to trial recruitment and success, but no such tools are validated for FTLD.

Objective

To evaluate the reliability and validity of smartphone-based cognitive measures for remote FTLD evaluations.

Design, Setting, and Participants

In this cohort study conducted from January 10, 2019, to July 31, 2023, controls and participants with FTLD performed smartphone application (app)-based executive functioning tasks and an associative memory task 3 times over 2 weeks. Observational research participants were enrolled through 18 centers of a North American FTLD research consortium (ALLFTD) and were asked to complete the tests remotely using their own smartphones. Of 1163 eligible individuals (enrolled in parent studies), 360 were enrolled in the present study; 364 refused and 439 were excluded. Participants were divided into discovery (n = 258) and validation (n = 102) cohorts. Among 329 participants with data available on disease stage, 195 were asymptomatic or had preclinical FTLD (59.3%), 66 had prodromal FTLD (20.1%), and 68 had symptomatic FTLD (20.7%) with a range of clinical syndromes.

Exposure

Participants completed standard in-clinic measures and remotely administered ALLFTD mobile app (app) smartphone tests.

Main Outcomes and Measures

Internal consistency, test-retest reliability, association of smartphone tests with criterion standard clinical measures, and diagnostic accuracy.

Results

In the 360 participants (mean [SD] age, 54.0 [15.4] years; 209 [58.1%] women), smartphone tests showed moderate-to-excellent reliability (intraclass correlation coefficients, 0.77-0.95). Validity was supported by association of smartphones tests with disease severity (r range, 0.38-0.59), criterion-standard neuropsychological tests (r range, 0.40-0.66), and brain volume (standardized β range, 0.34-0.50). Smartphone tests accurately differentiated individuals with dementia from controls (area under the curve [AUC], 0.93 [95% CI, 0.90-0.96]) and were more sensitive to early symptoms (AUC, 0.82 [95% CI, 0.76-0.88]) than the Montreal Cognitive Assessment (AUC, 0.68 [95% CI, 0.59-0.78]) (z of comparison, -2.49 [95% CI, -0.19 to -0.02]; P = .01). Reliability and validity findings were highly similar in the discovery and validation cohorts. Preclinical participants who carried pathogenic variants performed significantly worse than noncarrier family controls on 3 app tasks (eg, 2-back β = -0.49 [95% CI, -0.72 to -0.25]; P < .001) but not a composite of traditional neuropsychological measures (β = -0.14 [95% CI, -0.42 to 0.14]; P = .32).

Conclusions and Relevance

The findings of this cohort study suggest that smartphones could offer a feasible, reliable, valid, and scalable solution for remote evaluations of FTLD and may improve early detection. Smartphone assessments should be considered as a complementary approach to traditional in-person trial designs. Future research should validate these results in diverse populations and evaluate the utility of these tests for longitudinal monitoring.

Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration

The pathophysiological mechanisms driving disease progression of frontotemporal lobar degeneration (FTLD) and corresponding biomarkers are not fully understood. We leveraged aptamer-based proteomics (> 4,000 proteins) to identify dysregulated communities of co-expressed cerebrospinal fluid proteins in 116 adults carrying autosomal dominant FTLD mutations (C9orf72, GRN, MAPT) compared to 39 noncarrier controls. Network analysis identified 31 protein co-expression modules. Proteomic signatures of genetic FTLD clinical severity included increased abundance of RNA splicing (particularly in C9orf72 and GRN) and extracellular matrix (particularly in MAPT) modules, as well as decreased abundance of synaptic/neuronal and autophagy modules. The generalizability of genetic FTLD proteomic signatures was tested and confirmed in independent cohorts of 1) sporadic progressive supranuclear palsy-Richardson syndrome and 2) frontotemporal dementia spectrum syndromes. Network-based proteomics hold promise for identifying replicable molecular pathways in adults living with FTLD. 'Hub' proteins driving co-expression of affected modules warrant further attention as candidate biomarkers and therapeutic targets.

Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are increasingly used to forecast progression trajectories of cognitive decline caused by preclinical and prodromal Alzheimer's disease (AD). Many existing studies have explored the potential of these two distinct modalities with diverse machine and deep learning approaches. But successfully fusing MRI and PET can be complex due to their unique characteristics and missing modalities. To this end, we develop a hybrid multimodality fusion (HMF) framework with cross-domain knowledge transfer for joint MRI and PET representation learning, feature fusion, and cognitive decline progression forecasting. Our HMF consists of three modules: 1) a module to impute missing PET images, 2) a module to extract multimodality features from MRI and PET images, and 3) a module to fuse the extracted multimodality features. To address the issue of small sample sizes, we employ a cross-domain knowledge transfer strategy from the ADNI dataset, which includes 795 subjects, to independent small-scale AD-related cohorts, in order to leverage the rich knowledge present within the ADNI. The proposed HMF is extensively evaluated in three AD-related studies with 272 subjects across multiple disease stages, such as subjective cognitive decline and mild cognitive impairment. Experimental results demonstrate the superiority of our method over several state-of-the-art approaches in forecasting progression trajectories of AD-related cognitive decline.

Attention-Guided Autoencoder for Automated Progression Prediction of Subjective Cognitive Decline With Structural MRI

Subjective cognitive decline (SCD) is the preclinical stage of Alzheimer's disease (AD) which happens even earlier than mild cognitive impairment (MCI). Progressive SCD will convert to MCI with the potential of further evolving to AD. Therefore, early identification of progressive SCD with neuroimaging techniques (eg, structural MRI) is of great clinical value for early intervention of AD. However, existing MRI-based machine/deep learning methods usually suffer the small-sample-size problem and lack interpretability. To this end, we propose an interpretable autoencoder model with domain transfer learning (IADT) for progression prediction of SCD. Firstly, the proposed model can leverage MRIs from both the target domain (eg., SCD) and auxiliary domains (, AD and NC) for progressive SCD identification. Besides, it can automatically locate the disease-related brain regions of interest (defined in brain atlases) through an attention mechanism, which shows good interpretability. In addition, the IADT model is straightforward to train and test with only 5  ∼ 10 seconds on CPUs and is suitable for medical tasks with small datasets. Extensive experiments on the publicly available ADNI dataset and a private CLAS dataset have demonstrated the effectiveness of the proposed method.

Alzheimer's Disease Progressively Reduces Visual Functional Network Connectivity

Background:

Postmortem studies of brains with Alzheimer’s disease (AD) not only find amyloid-beta (Aβ) and neurofibrillary tangles (NFT) in the visual cortex, but also reveal temporally sequential changes in AD pathology from higher-order association areas to lower-order areas and then primary visual area (V1) with disease progression.

Objective:

This study investigated the effect of AD severity on visual functional network.

Methods:

Eight severe AD (SAD) patients, 11 mild/moderate AD (MAD), and 26 healthy senior (HS) controls undertook a resting-state fMRI (rs-fMRI) and a task fMRI of viewing face photos. A resting-state visual functional connectivity (FC) network and a face-evoked visual-processing network were identified for each group.

Results:

For the HS, the identified group-mean face-evoked visual-processing network in the ventral pathway started from V1 and ended within the fusiform gyrus. In contrast, the resting-state visual FC network was mainly confined within the visual cortex. AD disrupted these two functional networks in a similar severity dependent manner: the more severe the cognitive impairment, the greater reduction in network connectivity. For the face-evoked visual-processing network, MAD disrupted and reduced activation mainly in the higher-order visual association areas, with SAD further disrupting and reducing activation in the lower-order areas.

Conclusion:

These findings provide a functional corollary to the canonical view of the temporally sequential advancement of AD pathology through visual cortical areas. The association of the disruption of functional networks, especially the face-evoked visual-processing network, with AD severity suggests a potential predictor or biomarker of AD progression.

Alzheimer's Disease Progressively Alters the Face-Evoked Visual-Processing Network

BACKGROUND

Postmortem studies of Alzheimer's disease (AD) brains not only find amyloid-β (Aβ) and neurofibrillary tangles (NFT) in the primary and associative visual cortical areas, but also reveal a temporally successive sequence of AD pathology beginning in higher-order visual association areas, followed by involvement of lower-order visual processing regions with disease progression, and extending to primary visual cortex in late-stage disease. These findings suggest that neuronal loss associated with Aβ and NFT aggregation in these areas may alter not only the local neuronal activation but also visual neural network activity.

OBJECTIVE

Applying a novel method to identify the visual functional network and investigate the association of the network changes with disease progression.

METHODS

To investigate the effect of AD on the face-evoked visual-processing network, 8 severe AD (SAD) patients, 11 mild/moderate AD (MAD), and 26 healthy senior (HS) controls undertook a task-fMRI study of viewing face photos.

RESULTS

For the HS, the identified group-mean visual-processing network in the ventral pathway started from V1 and ended within the fusiform gyrus. In contrast, this network was disrupted and reduced in the AD patients in a disease-severity dependent manner: for the MAD patients, the network was disrupted and reduced mainly in the higher-order visual association areas; for the SAD patients, the network was nearly absent in the higher-order association areas, and disrupted and reduced in the lower-order areas.

CONCLUSION

This finding is consistent with the current canonical view of the temporally successive sequence of AD pathology through visual cortical areas.

Use of Flutemetamol F 18-Labeled Positron Emission Tomography and Other Biomarkers to Assess Risk of Clinical Progression in Patients With Amnestic Mild Cognitive Impairment

Importance

Patients with amnestic mild cognitive impairment (aMCI) may progress to clinical Alzheimer disease (AD), remain stable, or revert to normal. Earlier progression to AD among patients who were β-amyloid positive vs those who were β-amyloid negative has been previously observed. Current research now accepts that a combination of biomarkers could provide greater refinement in the assessment of risk for clinical progression.

Objective

To evaluate the ability of flutemetamol F 18 and other biomarkers to assess the risk of progression from aMCI to probable AD.

Design, Setting, and Participants

In this multicenter cohort study, from November 11, 2009, to January 16, 2014, patients with aMCI underwent positron emission tomography (PET) at baseline followed by local clinical assessments every 6 months for up to 3 years. Patients with aMCI (365 screened; 232 were eligible) were recruited from 28 clinical centers in Europe and the United States. Physicians remained strictly blinded to the results of PET, and the standard of truth was an independent clinical adjudication committee that confirmed or refuted local assessments. Flutemetamol F 18-labeled PET scans were read centrally as either negative or positive by 5 blinded readers with no knowledge of clinical status. Statistical analysis was conducted from February 19, 2014, to January 26, 2018.

Interventions

Flutemetamol F 18-labeled PET at baseline followed by up to 6 clinical visits every 6 months, as well as magnetic resonance imaging and multiple cognitive measures.

Main Outcomes and Measures

Time from PET to probable AD or last follow-up was plotted as a Kaplan-Meier survival curve; PET scan results, age, hippocampal volume, and aMCI stage were entered into Cox proportional hazards logistic regression analyses to identify variables associated with progression to probable AD.

Results

Of 232 patients with aMCI (118 women and 114 men; mean [SD] age, 71.1 [8.6] years), 98 (42.2%) had positive results detected on PET scan. By 36 months, the rates of progression to probable AD were 36.2% overall (81 of 224 patients), 53.6% (52 of 97) for patients with positive results detected on PET scan, and 22.8% (29 of 127) for patients with negative results detected on PET scan. Hazard ratios for association with progression were 2.51 (95% CI, 1.57-3.99; P < .001) for a positive β-amyloid scan alone (primary outcome measure), 5.60 (95% CI, 3.14-9.98; P < .001) with additional low hippocampal volume, and 8.45 (95% CI, 4.40-16.24; P < .001) when poorer cognitive status was added to the model.

Conclusions and Relevance

A combination of positive results of flutemetamol F 18-labeled PET, low hippocampal volume, and cognitive status corresponded with a high probability of risk of progression from aMCI to probable AD within 36 months.

Analysis of conversion of Alzheimer’s disease using a multi-state Markov model

With rapid aging of world population, Alzheimer’s disease is becoming a leading cause of death after cardiovascular disease and cancer. Nearly 10% of people who are over 65 years old are affected by Alzheimer’s disease. The causes have been studied intensively, but no definitive answer has been found. Genetic predisposition, abnormal protein deposits in brain, and environmental factors are suspected to play a role in the development of this disease. In this paper, we model progression of Alzheimer’s disease using a multi-state Markov model to investigate the significance of known risk factors such as age, apolipoprotein E4, and some brain structural volumetric variables from magnetic resonance imaging scans (e.g., hippocampus, etc.) while predicting transitions between different clinical diagnosis states. With the Alzheimer’s Disease Neuroimaging Initiative data, we found that the model with age is not significant (p = 0.1733) according to the likelihood ratio test, but the apolipoprotein E4 is a significant risk factor, and the examination of apolipoprotein E4-by-sex interaction suggests that the apolipoprotein E4 link to Alzheimer’s disease is stronger in women. Given the estimated transition probabilities, the prediction accuracy is as high as 0.7849.

Performance of [18F]flutemetamol amyloid imaging against the neuritic plaque component of CERAD and the current (2012) NIA-AA recommendations for the neuropathologic diagnosis of Alzheimer's disease

Introduction

Performance of the amyloid tracer [¹⁸F]flutemetamol was evaluated against three pathology standard of truth (SoT) measures including neuritic plaques (CERAD “original” and “modified” and the amyloid component of the 2012 NIA-AA guidelines).

Methods

After [¹⁸F]flutemetamol imaging, 106 end-of-life patients who died underwent postmortem brain examination for amyloid plaque load. Blinded positron emission tomography scan interpretations by five independent electronically trained readers were compared with pathology measures.

Results

By SoT, sensitivity and specificity of majority image interpretations were, respectively, 91.9% and 87.5% with “original CERAD,” 90.8% and 90.0% with “modified CERAD,” and 85.7% and 100% with the 2012 NIA-AA criteria.

Discussion

The high accuracy of either CERAD criteria suggests that [¹⁸F]flutemetamol predominantly reflects neuritic amyloid plaque density. However, the use of CERAD criteria as the SoT can result in some false-positive results because of the presence of diffuse plaques, which are accounted for when the positron emission tomography read is compared with the 2012 NIA-AA criteria.

•

Determination of the accuracy of [¹⁸F]flutemetamol image read against Aβ at autopsy.

•

High sensitivity and specificity to 3 neuropathologic criteria as Standards of Truth.

•

Images are 100% specific when the SoT reflects both neuritic and diffuse plaques.

•

This study has the largest autopsy validation cohort for Aβ PET tracers to date.

Phase 3 trial of flutemetamol labeled with radioactive fluorine 18 imaging and neuritic plaque density

IMPORTANCE

In vivo imaging of brain β-amyloid, a hallmark of Alzheimer disease, may assist in the clinical assessment of suspected Alzheimer disease.

OBJECTIVE

To determine the sensitivity and specificity of positron emission tomography imaging with flutemetamol injection labeled with radioactive fluorine 18 to detect β-amyloid in the brain using neuropathologically determined neuritic plaque levels as the standard of truth.

DESIGN, SETTING, AND PARTICIPANTS

Open-label multicenter imaging study that took place at dementia clinics, memory centers, and hospice centers in the United States and England from June 22, 2010, to November 23, 2011. Participants included terminally ill patients who were 55 years or older with a life expectancy of less than 1 year.

INTERVENTIONS

Flutemetamol injection labeled with radioactive fluorine 18 (Vizamyl; GE Healthcare) administration followed by positron emission tomography imaging and subsequent brain donation.

MAIN OUTCOMES AND MEASURES

Sensitivity and specificity of flutemetamol injection labeled with radioactive fluorine 18 positron emission tomography imaging for brain β-amyloid. Images were reviewed without and with computed tomography scans and classified as positive or negative for β-amyloid by 5 readers who were blind to patient information. In patients who died, neuropathologically determined neuritic plaque levels were used to confirm scan interpretations and determine sensitivity and specificity.

RESULTS

Of 176 patients with evaluable images, 68 patients (38%) died during the study, were autopsied, and had neuritic plaque levels determined; 25 brains (37%) were β-amyloid negative; and 43 brains (63%) were β-amyloid positive. Imaging was performed a mean of 3.5 months (range, 0 to 13 months) before death. Sensitivity without computed tomography was 81% to 93% (median, 88%). Median specificity was 88%, with 4 of 5 of the readers having specificity greater than 80%. When scans were interpreted with computed tomography images, sensitivity and specificity improved for most readers but the differences were not significant. The area under the receiver operating curve was 0.90. There were no clinically meaningful findings in safety parameters.

CONCLUSIONS AND RELEVANCE

This study showed that flutemetamol injection labeled with radioactive fluorine 18 was safe and had high sensitivity and specificity in an end-of-life population. In vivo detection of brain β-amyloid plaque density may increase diagnostic accuracy in cognitively impaired patients.

Effects of a computer-based cognitive exercise program on age-related cognitive decline

We developed a 'senior friendly' suite of online 'games for learning' with interactive calibration for increasing difficulty, and evaluated the feasibility of a randomized clinical trial to test the hypothesis that seniors aged 60-80 can improve key aspects of cognitive ability with the aid of such games. Sixty community-dwelling senior volunteers were randomized to either an online game suite designed to train multiple cognitive abilities, or to a control arm with online activities that simulated the look and feel of the games but with low level interactivity and no calibration of difficulty. Study assessment included measures of recruitment, retention and play-time. Cognitive change was measured with a computerized assessment battery administered just before and within two weeks after completion of the six-week intervention. Impediments to feasibility included: limited access to in-home high-speed internet, large variations in the amount of time devoted to game play, and a reluctance to pursue more challenging levels. Overall analysis was negative for assessed performance (transference effects) even though subjects improved on the games themselves. Post hoc analyses suggest that some types of games may have more value than others, but these effects would need to be replicated in a study designed for that purpose. We conclude that a six-week, moderate-intensity computer game-based cognitive intervention can be implemented with high-functioning seniors, but the effect size is relatively small. Our findings are consistent with Owen et al. (2010), but there are open questions about whether more structured, longer duration or more intensive 'games for learning' interventions might yield more substantial cognitive improvement in seniors.

fMRI evidence that precision ophthalmic tints reduce cortical hyperactivation in migraine

BACKGROUND

Certain patterns can induce perceptual illusions/distortions and visual discomfort in most people, headaches in patients with migraine, and seizures in patients with photosensitive epilepsy. Visual stimuli are common triggers for migraine attacks, possibly because of a hyperexcitability of the visual cortex shown in patients with migraine. Precision ophthalmic tints (POTs) are claimed to reduce perceptual distortions and visual discomfort and to prevent migraine headaches in some patients. We report an fMRI visual cortical activation study designed to investigate neurological mechanisms for the beneficial effects of POTs in migraine.

METHODS

Eleven migraineurs and 11 age- and sex-matched non-headache controls participated in the study using non-stressful and stressful striped patterns viewed through gray, POT, and control coloured lenses.

RESULTS

For all lenses, controls and migraineurs did not differ in their response to the non-stressful patterns. When the migraineurs wore gray lenses or control coloured lenses, the stressful pattern resulted in activation that was greater than in the controls. There was also an absence of the characteristic low-pass spatial frequency (SF) tuning in extrastriate visual areas. When POTs were worn, however, both cortical activation and SF tuning were normalized. Both when observing the stressful pattern and under more typical viewing conditions, the POTs reduced visual discomfort more than either of the other two lenses.

CONCLUSION

The normalization of cortical activation and SF tuning in the migraineurs by POTs suggests a neurological basis for the therapeutic effect of these lenses in reducing visual cortical hyperactivation in migraine.

Regional white matter pathology in mild cognitive impairment: differential influence of lesion type on neuropsychological functioning

BACKGROUND AND PURPOSE

Associations between regional white matter lesion pathology and neuropsychological performance across the aging spectrum are not well understood and, to date, research has been largely contradictory and inconclusive. The current study set out to clarify some of the inconsistencies in the literature by relating volumetric analyses of white matter lesions (deep white matter lesions and periventricular lesions) to neuropsychological performance in a large clinical sample of older adults diagnosed with mild cognitive impairment.

METHODS

Seventy older adults with mild cognitive impairment were administered a comprehensive neuropsychological battery. White matter lesions identified on T2-weighted FLAIR images were quantified using a semi-automated volumetric approach (pixel thresholding).

RESULTS

Results showed that, in contrast to performance on memory and naming tasks, total white matter lesions strongly predicted executive impairments, slowed processing speed, and visuospatial/construction difficulties. In addition, separate regression analyses demonstrated that results were primarily accounted for by deep white matter lesions (but not periventricular lesions), most likely due to frontal-subcortical circuitry disruption. Moreover, deep white matter lesions, but not periventricular lesions, significantly predicted overall poorer neuropsychological functioning after controlling for age, education, and level of depression.

CONCLUSIONS

Taken together, findings demonstrate a differential influence of lesion type on cognitive impairment in mild cognitive impairment and implicate deep white matter lesions as being most detrimental in terms of neuropsychological functioning. Clinical, theoretical, and methodological implications of these results are discussed.

Can patients with Alzheimer's disease learn a category implicitly?

Can a person with a damaged medial-temporal lobe learn a category implicitly? To address this question, we compared the performance of participants with mild Alzheimer's disease (AD) to that of age-matched controls in a standard implicit learning task. In this task, participants were first presented a series of objects, then told the objects formed a category, and then had to categorize a long sequence of test items [Knowlton B. J., Squire L. R. (1993). The learning of categories: parallel brain systems for item memory and category knowledge. Science, 262, 1747-1749]. We tested the hypotheses that: (1) both Control and AD participants would show evidence for implicit learning after the unwanted contribution of learning during test is removed; (2) the degree of implicit learning is the same for AD and Control participants; (3) training with exemplars that are highly similar to an unseen prototype will lead to better implicit category learning than training with exemplars that are less similar to a prototype. With respect to the first hypothesis, we found that both AD and Control participants performed better on tests of implicit learning than could be attributed to just learning on test trials. We found no clear means for evaluating our second hypothesis, and argue that comparisons of the degree of implicit learning between patient and control groups in this paradigm are confounded by the contribution of other memory systems. In line with the third hypothesis, only training with similar exemplars resulted in significant implicit category learning for AD participants.

Mild cognitive impairments predict dementia in nondemented elderly patients with memory loss

BACKGROUND

Some elderly individuals exhibit significant memory deficits but do not have dementia because their general intellect is preserved and they have no impairments in everyday activities. These symptoms are often a precursor to Alzheimer disease (AD), but sometimes dementia does not occur, even after many years of observation. There is currently no reliable way to distinguish between these 2 possible outcomes in an individual patient. We hypothesized that clear impairments in at least 1 cognitive domain in addition to memory would help identify those who will progress to AD.

OBJECTIVE

To determine whether nondemented patients with impairments in memory and other domains are more likely than those with memory impairment alone to develop AD.

DESIGN AND METHODS

In a retrospective study, we evaluated 48 nondemented, nondepressed patients with clinical and psychometric evidence of memory impairment who were followed up for 2 or more years. Age-adjusted normative criteria were used to identify whether additional impairments were present in language, attention, motor visuospatial function, and verbal fluency at this initial evaluation. The presence or absence of dementia after 2 years and at the most recent neurological evaluation was compared in subjects with normal scores in all 4 of these cognitive areas apart from memory (M-) and those with impairment in 1 or more of these areas (M+). Outcomes were adjusted for age, intelligence at initial evaluation, and years of education.

RESULTS

Of the 48 nondemented patients with memory loss, 17 met M- criteria, leaving 31 in the M+ group. Deficits in block design were the most frequent abnormality other than memory loss. At the 2-year follow-up, 1 M- subject (6%) had progressed to AD, whereas 15 (48%) of the M+ group had progressed to AD (P =.003). At the most recent follow-up (mean +/- SD, 4.0 +/- 2.0 years), 4 (24%) of the M- patients progressed to AD compared with 24 (77%) of the M+ patients (P<.001).

CONCLUSIONS

Among nondemented elderly patients, memory loss alone rarely progresses to dementia in the subsequent 2 years. However, the risk of dementia is significantly increased among patients with clear cognitive impairments beyond memory loss. Further study is needed to determine whether patients with impairments limited to memory loss have a distinctive clinical course or pathophysiology.